The process of adjusting and becoming accustomed to hearing aids may be best facilitated by providing a hearing aid trial period. Globally, there are no standardised frameworks or regulations on the recommended hearing aid trial period. The main purpose of this study was to explore audiologists’ hearing aid trialing practices. A cross sectional study employed a quantitative, descriptive design to formulate the study. Ninety-five audiologists’ were recruited by means of a purposive sampling strategy. Data were collected through the use of an online survey via Google forms. A pilot study was conducted prior to the commencement of the main study in order to ensure reliability of the main study. The results indicated that two weeks was the most recommended duration of a trial period from audiologists’. Majority of audiologists’ (72.63%) offer hearing aid trials to their patients. Most audiologists’ who offer hearing aid trials choose to trial their patients with two different hearing aids. Findings revealed a need for trialing periods to become standard practice by audiologists’ when fitting hearing aids. Not enough audiologists’ are providing this service. According to the World Health Organization (WHO), it is estimated that approximately 5% (432 million adults and 34 million children) of the world’s population experience a disabling hearing loss. This figure is expected to rise significantly by 2050, with around 1 in every 10 people, roughly 700 million individuals, projected to have a disabling hearing loss [Despite the high prevalence of hearing loss in the elderly population worldwide, the use and uptake of hearing aids is still poor ,5. A pleHearing aids have the potential to significantly improve an individual’s access to sound in various environments  providinGlobally, the practice of conducting hearing aid trial seems to be an ‘up-in-the-air’ process, lacking standardised frameworks or regulations regarding the recommended duration of the trial period. This absence of standardised guidelines on the hearing aid trial period as well as the number hearing aids available for trial leads to inconsistency across different parts of the world .As mentioned earlier, hearing aid users need several weeks or more to acclimatize and ‘re-train the brain’ to identify certain frequencies and sounds. It is for this reason that in the United States, it is against the law to not offer trial periods to potential users as people with different types of hearing loss respond differently to hearing aids . In counA lack of standardised hearing aid trials may contribute to unintended consequences including:unequal access: some individuals may not have the opportunity to adequately assess the suitability and effectiveness of hearing aids before making a decision. This is particularly true for adults in lower socioeconomic settings, resulting in a lower prevalence of hearing aid use .limited evaluation time: without a standardized trial period, patients may have limited time to assess the benefits and limitations of hearing aids, potentially leading to dissatisfaction or suboptimal outcomes. One researcher argued that not all adults provided with hearing aids use them, wear them regularly, or are satisfied with them .Varied professional guidance: hearing aid trial periods often involve guidance and support from audiologists’ or hearing healthcare professionals. Without standardization, the level of professional guidance during the trial period may differ, potentially impacting the quality of fitting, counselling and adjustments made to optimize the hearing aid settings for individual users. According to Ismail et al, hearing healthcare professionals can exert an important impact on patient outcomes .Increased financial risk: hearing aids are expensive, and most insurance plans don’t cover routinely cover them, and in most cases, users typically pay for aids and fittings out of pocket . WithoutQuality Assurance: consistency across manufactures in describing the features of hearing aids enables users to compare features and not solely rely on audiologists’, and hearing aid dispensers for that information  ProvidinDespite the high prevalence of disabling hearing loss in the African region, there is a significant shortage of audiologists’ to provide hearing healthcare services. Approximately 78% of countries in Africa have less than one audiologist per million population, whereas 52% of European countries have more than 10 audiologists’ per million . This scIn South Africa the healthcare system is divided into public and private sectors. Around 84% of the population relies on public healthcare services, while 16% affords private healthcare ,23. As aEvidence suggests that audiologists’ who offer hearing aid trials report a higher success rate in hearing aid sales and increased patient satisfaction ,27. A stTo investigate audiologists’ practices related to hearing aid trials.To examine the practices used by audiologists’ for fitting hearing aidsTo analyse the approaches employed by audiologists’ during the hearing aid trialing processTo determine the average number of hearing aids typically trialed by audiologists’To assess the average duration of a hearing aid trial conducted by audiologists’This was a two-phased explanatory sequential mixed methods cross-sectional study. Phase one was quantitative in nature and employed a questionnaire to capture audiologist’s practices in providing hearing aid trials. This was followed by a qualitative phase where interviews were conducted with hearing aid users to explore their experiences with undergoing a hearing aid trial. However, for the current study, we are reporting on Phase 1, exploring the practices of audiologists’ in providing hearing aid trials.Purposive sampling was used to specifically recruit audiologists’ practising in South Africa and registered with the Health Professions Council of South Africa (HPCSA). A prior sample size was not determined for this study due to the difficult in ascertaining the actual number of practicing audiologists’ in South Africa. The sample size could only be determined based on audiologists’ registered with the HPCSA or the professional organisations, which in most cases is inaccurate as some people may be registered but may not be practising or may be practising and not be registered with the professional association boards. Furthermore, this study was administered online, the researcher had to rely on the professional organisations to act as gatekeepers in distributing the survey link to potential participants. This provided a challenge as to the number of potential participants that could be reached. As such, it was difficult to estimate the number of participants to achieve an adequate statistical power and precision to ensure the study’s results are statistically meaningful and reliable. A sample of 95 audiologists’ was obtained . ParticiFor the first phase of the study, data were collected by means of an online survey conducted via Google Forms . Google Three audiologists’ meeting the inclusion criteria were approached and requested to participate in the pilot phase of the study by direct invitation through the researcher’s network. The first audiologist was requested to assess the content, language and length of the survey. Feedback recommended: i) to include an option that ‘more than one option’ may be given on three of the survey questions; ii) Additionally, to add an option to elaborate on ‘no’ responses for three of the survey questions. Once the suggested corrections were made, the survey was then given to a second audiologist to provide feedback on the length and content of the survey. A change to the duration of the survey from ten minutes to five minutes was recommended as the completion time was quicker than anticipated by the researcher. Lastly, the content and questions were deemed straight forward. The third audiologist was recruited for the second phase of the study. Participants in the pilot study were required to meet the same inclusion criteria for the research study (as mentioned above). These two audiologists’ were excluded from the main study.Ethical clearance was obtained from the University’s Human Research Ethics Committee  (Protocol number: M191054). Participants provided informed consent by clicking the ‘continue’ button on Google Forms after reading and understanding the information sheet. Participants were thus made aware that informed consent would be assumed once they proceeded with the study after reading the information sheet. All methods were carried out in accordance with relevant guidelines and regulations.After obtaining ethics clearance from the Human Research Ethics Committee  (Protocol number: M191054), professional organizations, namely the South African Association of Audiologists’ (SAAA) and South African Speech-Language-Hearing Association (SASHLA) were approached via email and requested to distribute the self-developed survey to audiologists’ on their emailing lists. Additionally, audiologists’ on social media platforms, such as Facebook groups and LinkedIn, were targeted. The first page of the survey consisted of the information letter, where the purpose, inclusion criteria and study requirements were detailed. Potential participants were advised that clicking on the start button at the end of the information letter meant they were giving consent to participate on the online survey. Upon clicking the start button, participants were directed to the survey question on Google Forms. Data collection took place over a period of six months between February and July 2020.Data were captured in Microsoft Excel and Microsoft Word. Both close-ended and open-ended questions were analysed quantitatively using descriptive analysis. Descriptive analysis is concerned with summarizing data with large amounts of numbers that could not otherwise be comprehended by looking at them. As such, frequency tables and graphs were used to summarize the data more comprehensively. This allowed the researcher to capture information and analyse it more efficiently.From the 95 audiologists’ who participated in the survey, 94.74% (n = 90) provided hearing aid fittings as part of their services, while 5% (n = 5) do not fit hearing aids . Table 2Audiologists’ were asked how they choose the hearing aid for their patients or if patients choose their own hearing aids. The results revealed that 28.42% (n = 27) of audiologists’ choose the hearing aid for their patients based on their audiogram. On the other hand, 54.74% (n = 52) of audiologists’ choose the hearing aids, however, in consultation with their patients. This suggests that these audiologists’ do consider the patient’s needs as well as the situation . The reasons that were given on which hearing aid to choose are indicated below .Taking into account the patient’s hearing loss when choosing a hearing aid is one of the most important decisions within the hearing aid fitting process. Audiologists’ were further asked when choosing a hearing aid, what is the most common determining factor for the hearing aid user when deciding on which hearing aid to choose from. As seen in The audiologists’ were asked to state possible explanations as to why some patients may return their hearing aids, with the option to choose more than one answer . Top on More than half of the audiologists’ reported that their patients complained about background noise as a negative factor whilst wearing hearing aids with feedback, echo and loudness being the next most common complaint. The least reported complaints from hearing aid users were muffled, blocked and sharpness/tinniness of the hearing aids.Audiologists’ in this study were asked about their perceptions on whether or not audiologists’ should be offering hearing aid trial periods. The results revealed 93.68% (n = 89) believe that audiologists’ should offer hearing aid trials as part of their service-delivery to the patients. On the other hand, 6.32% (n = 6) believe that audiologists’ should not provide trials based on the following reasons:The hearing aid users may lose or break the hearing aid during the trial period.Audiologists’ do not have the opportunity to trial hearing aids in the government sector.Hearing aid trials were too expensive and time consuming.Hearing aid trials are an insurance risk to the practice.And lastly, as described by one participant: “the adjustment time for the brain to adapt to the new hearing aids as well as the multiple listening opportunities are not accommodated by the trial period”.Audiologists’ were further asked to recommend a suitable period for hearing aid trials. Eighty-nine participants (93.68%) who felt that the trial period is a necessity made their recommendations . MajoritIn establishing audiologists’ methods of hearing aid trialing practices, the results indicated that 72.63% (n = 69) of audiologists’ reported offering trialing of hearing aids as mandatory practice within their services, while 27.37% (n = 26) did not. From those who did not offer hearing trials, n = 15 reported working in a government hospital, clinic, or rural area. Reportedly, hearing aid trials were logistically impossible and thus not feasible to conduct. Some reasons for this were due to: a limited amount of available staff, long waiting lists, not being stated in the policy, not being permitted, financial implications, insurance purposes, and travel limitations from the patient’s side.Majority of audiologists’ who offered hearing aid trials offered two different hearing aids for trialing  while others preferred to trial only one  hearing aid. Only 1.45% (n = 1) participant reported trialing 3 or more hearing aids per trial period. More than half of the participants  reported trialing between one to two hearing aids but it was patient-specific and circumstantial.To determine a suitable number of hearing aids that could be tested per trial period, the results revealed a large number of audiologists’ offered more than one hearing aid trial to their patients which can be seen in Audiologists’ were further asked to describe their thought process when deciding to trial hearing aids. Only n = 31 participants responded to this question. The following responses were prominent:Some audiologists’  reported only allowing patients to trial hearing aids on request while 45.16% (n = 14) reported offering the hearing aid trial without it being requested by the patient. After having trialed one, two, or three hearings aids, audiologists’ reported that 77.42% (n = 24) of patients end up choosing the first hearing aid they trialed. On the other hand, 9.68% (n = 3) choose the second hearing aid and only 6.45% (n = 2) choose the third or last hearing aid that they trialed. These results indicated a preference for the first hearing aid trialed.In South Africa, health care professionals such as audiologists’ and speech therapists in urbanized and densely populated provinces such as Gauteng and Western Cape tend to provide private services more than the public sector services . For theTo date, the private sector in South Africa dominates the healthcare sector, attracting majority of professionals, even though the country has a larger public sector . The demWith new advances in technology such as the digital hearing aid, there are a number of advantages over the “old school” analogue hearing aids. These include: digital feedback and noise reduction, increased comfort, smaller size, open fit designs, and speech enhancement . With thHong et al stated that the patient and the audiologist should be equally involved in the hearing aid fitting process and that they both have responsibilities regarding hearing aid selection and fitting . If eachThus, according to the results, the researcher is in agreement with the first mentioned study  whereby Audiologists’ in the study stated that their patients rarely return their hearing aids once fitted, or, that one in every five returned their hearing aids at most. Audiologists’ reported the following reason for the return of hearing aid: death (the hearing aids are returned by a family member), discomfort of the hearing aid, financial implications, dissatisfaction from the hearing aid and that the hearing aids didn’t reach their expectations. Some similarities can be seen in a research study on clinical reasons for hearing aid returns in Korea from 1138 hearing aid users who were prescribed hearing aids over a three-year period. They found that ineffectiveness of the hearing aid, noise (such as feedback), over-amplification, managing and handling the devices, and lastly, financial aspects were the most common reported reasons for hearing aid returns. Despite advances in technology, 3–16% of individuals in their study returned the hearing aids over the three-year period .More than half of the audiologists’ in the current study reported that their patients complained about background noise as a negative factor whilst wearing hearing aids with feedback, echo and loudness being the next most common complaints. A review of research study found that background noises and surrounding sounds were the most common complaints reported by hearing aid users .After years of experiencing communication difficulties, which undoubtedly impacts a person’s life, the decision to try hearing aids brings about all kinds of expectations and fears. These occur due to the hearing loss, and not the hearing aids . This stAs mentioned earlier, a hearing aid trial period may convince individuals to try amplification who otherwise would not do so. Almost all of the audiologist participants in this study reported that audiologists’ should be offering trial periods for hearing aids in South Africa as part of their practices. With this being said, 72.63% of audiologists’ in the study actually provide hearing aid trials to their patients. Moreover, 54,84% of these audiologists’ reported only allowing patients to trial hearing aids on request, while the other 45,16% reported offering the hearing aid trials without it being requested by the patient. These results indicated that approximately 50% of audiologists’ offer hearing aid trials to their patients as part of their daily practices. Audiologists’ who offer trials reported a higher success rate in hearing aid sales and patient satisfaction ,27. One Some audiologists’ in the current study were concerned that they cannot guarantee the safe care and return of hearings aids, which poses insurance risk to their practices. Additionally, audiologists’ working in public hospitals or clinics in particular do not provide trial periods because of staff shortage, long waiting lists for hearing aids, no guarantee of return of hearing aid, and trials not being part of their standard practice. There is a shortage of staff and available resources in the rural and government sectors in South Africa which unfortunately leads to no opportunity for trial periods to be provided.The population of individuals who have a hearing loss could be impacted from communication difficulties and a restricted social life. These negative effects of hearing loss on QoL can be improved with the use of hearing aids. The hearing aid users who adjust successfully to hearing aids typically experience higher self-esteem compared to those who do not wear their hearing aids regularly and consistently . All of According to literature, the most common duration of a trial period is a minimum of 30 days ,14. HoweOne research study complimented the current study in declaring that hearing aid users have difficulty adjusting to the hearing aids and individuals who did not wear their hearing aids regularly needed a longer time to adjust to them and should persevere during the initial period of obtaining hearing aids . AudioloIn cases where up to three brands of hearing aids are trialed, audiologists’ reported that more than half of patients end up choosing the first hearing aid they trialed. These results indicate a preference for the first hearing aid trialed. This may be due to the users remembering the immediate benefits achieved from their first trial. Audiologists’ further stated that the average number of hearing aids they trial their patients on is two hearing aids, thus giving their patients a choice between two different brands or styles to choose from.Hearing aids are known to improve QoL. Hearing difficulty may be reduced with an effective, well programmed, hearing aid. This will also reduce the negative effects of social and emotional interactions caused by a hearing loss . With thBased on the findings of the current study, it can be concluded that providing hearing aid trials routinely is crucial in the rehabilitation process, while taking into account the availability of necessary resources. This study revealed 72.63% of audiologists’ provide hearing aid trials, with a smaller proportion incorporating hearing aid trials as part of their routine practice rather than solely upon request. In this study, audiologists’ agreed that offering a hearing aid trial before purchasing a hearing aid may yield desired outcomes. From an ethical standpoint, if hearing aids improve hearing aid outcomes and quality of life, audiologists’ should consider including hearing aid trials as part of their practices to enhance service delivery as healthcare professionals. While hearing aids trials demonstrate numerous benefits for users and could potentially become standard practice not only in South Africa but also internationally. As such, further research is recommended in this area.It is also worth noting that the provision of a hearing aid trial is not only beneficial to the users but also to the audiologists’ as well. Audiologists’ stand to benefit from every hearing aid purchased and successfully fitted. Therefore, it is important to identify factors that affect hearing aid usage to implement appropriate rehabilitation strategies and ensure greater utilization of hearing aids.Audiologists’ should play an active role in offering hearing aid trials to their patients, particularly first-time hearing aid users. Even though experienced hearing aid users may not feel confident with their hearing aids, it is important to note that technology is continuously advancing, offering improved features, sound quality, accessories, comfort and size. As such, hearing aid trials should still be offered to all hearing aid users, irrespective of their previous experience with hearing aids. There is a clear need to provide hearing aid trials to optimise outcomes for patients and ensure that they receive the most suitable and beneficial hearing aid solution.A small study sample of 95 audiologists’ who conduct hearing aid trials cannot be generalized to the greater audiology population who perform hearing aid trials. Additionally, the study is limited to the South African population and thus cannot be generalized world-wide.S1 Appendix(PDF)Click here for additional data file.


Numerous cathepsin B inhibitors have been developed and are under investigation as potential cancer treatments. They have been evaluated for their ability to inhibit cathepsin B activity and reduce tumor growth. However, they have shown critical limitations, including low anticancer efficacy and high toxicity, due to their low selectivity and delivery problems. In this study, we developed a novel peptide and drug conjugate (PDC)-based cathepsin B inhibitor using cathepsin-B-specific peptide (RR) and bile acid (BA). Interestingly, this RR and BA conjugate (RR–BA) was able to self-assemble in an aqueous solution, and as a result, it formed stable nanoparticles. The nano-sized RR–BA conjugate showed significant cathepsin B inhibitory effects and anticancer effects against mouse colorectal cancer (CT26) cells. Its therapeutic effect and low toxicity were also confirmed in CT26 tumor-bearing mice after intravenous injection. Therefore, based on these results, the RR–BA conjugate could be developed as an effective anticancer drug candidate for inhibiting cathepsin B in anticancer therapy. Cathepsin B is an important target in cancer research and has been widely studied for the past 20 years. It is a cysteine peptidase of the lysosomal cysteine protease family, which plays an important role in proteolysis. In the cathepsin family, cathepsin enzymes, such as cathepsin A, B, C, D, E, F, G, H, K, L, O, S, W, and Z, contribute to the development of several diseases, participating in multiple biological processes, such as apoptosis, autophagy, inflammation, and extracellular matrix remodeling . CathepsInvestigations into the cathepsin B enzyme in tumor tissue have attracted a lot of attention, since it plays a pivotal role in tumor invasion and metastasis. Much recent pharmacological research has been focused on targeting overexpressed cathepsin B in cancer cells ,10. ReseCathepsin B inhibitors are being actively investigated as potential anticancer agents due to their role in cancer cell invasion and migration. Targeting and inhibiting cathepsin B in cancer cells could have significant therapeutic implications ,27. SeveHere, we developed a cathepsin B inhibitor using a peptide and bile acid for anticancer therapy. Among the peptide sequences that cathepsin B can recognize, positively charged Arg-Arg (RR) was used as a cathepsin-B-specific peptide without any linkers. A hydrophobic bile acid (BA) was coupled via amide linkages to further enhance the inhibitory effects of the peptides. Out of several types of bile acids, ursodeoxycholic acid (UDCA), a safe drug approved by the US Food and Drug Administration (FDA), was used for this study. The nano-sized Arg-Arg and BA conjugate (RR–BA) can be self-assembled in an aqueous solution, forming nanoparticles due to the strong positive charge and the hydrophobicity of bile acids . This phN,N-diisopropylethylamine (DIPEA), anhydrous N,N-dimethylformamide (DMF), Dulbecco’s Modified Eagle’s Medium (DMEM), diethyl ether, N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride (EDC), N-hydroxysuccinimide (NHS), methanol, phosphate-buffered saline (PBS), trifluoroacetic acid (TFA), and ursodeoxycholic acid (UDCA) were purchased from Sigma-Aldrich . Fetal bovine serum (FBS) was obtained from Gibco . An Ez Cytox kit was obtained from DoGenBio . Neutral buffered formalin (10%) was purchased from HuBenTech . A Pierce BCA protein assay kit was obtained from Thermo Fisher Scientific . Chloroform was purchased from DAEJUNG .The C-terminal modified RR peptide  was synthesized by ANYGEN . Acetonitrile (ACN), antibiotic antimycotic solution, a cathepsin B activity assay kit, N,N-diisopropylethylamine (DIPEA) was further mixed in to avoid salt’s effects. The solution was continuously stirred at 600 rpm at room temperature for 24 h. After one day of incubation, the product was slowly precipitated in 14 mL of cold ether/chloroform (6:1) co-solvent twice. The precipitant was collected using a centrifuge at 2000 rpm for 4 min. After removing the upper solvent, the remaining solvent in the white powder was further removed for 25 min using a vacuum evaporator, then 5 mL of distilled water was added, and the sample was freeze-dried for 48 h. Next, the powder was dissolved in distilled water (1 mL) and then further purified via Sep-Pak C18 Plus cartridges  using distilled water (100%) and acetonitrile/water (50/50%) cosolvent. The solution was finally freeze-dried to obtain a white powder.The cathepsin-B-specific and hydrophilic RR peptide (RR-NHEt) was synthesized by ANYGEN. Then it was directly conjugated with ursodeoxycholic acid (UDCA) via a one-step amide bond coupling reaction. A total of 50 mg of RR peptide (RR-NHEt) was dissolved in anhydrous DMF, and then 27.5 mg of ursodeoxycholic acid was added to the peptide-containing DMF. After 5 min stirring, 8.1 mg of N-Hydroxysuccinimide (NHS) and 40.2 mg of N-(3-Dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride (EDC) were added to the solution for the coupling reaction. Then, 4.9 µL of 3OH/CH3COCH3/CH3COOH .The purity of the final product was confirmed via reverse-phase high-performance liquid chromatography (RP-HPLC) . All analyses were carried out on an Eclipse Plus C18 reverse column  by gradient elution with distilled water (90% to 10%) and acetonitrile (10% to 90%) containing 0.1% trifluoroacetic acid (TFA) as mobile phases at a flow rate of 1.0 mL/min. The product was measured using an ultraviolet–visible (UV–Vis) detector at 224 nm. Additionally, in all processes of synthesis, the mixture was observed via normal-phase thin-layer chromatography (TLC) with a mobile phase CH6).For mass spectroscopy, liquid chromatography–mass spectrometry  was used to identify the final product (RR–BA). In the scan mode, atmospheric pressure ionization-electrospray (API-ES) techniques were used to detect the molecular weights of RR–BA molecules with a capillary voltage of 3000 V with drying gas (350 °C). The final products were also confirmed via one-dimensional proton nuclear magnetic resonance (1D proton NMR) analysis, using a 500 MHz NMR spectrometer  at a concentration of 5 mg/600 μL with dimethylsulfoxide  analysis. The RR–BA materials were dissolved in filtered normal saline (0.9% NaCl) at 1 mg/mL for DLS measurement. The zeta potential value of RR–BA (1 mg/mL) in saline was also evaluated using the Zetasizer Nano method. The spherical morphology of the self-assembled RR–BA nanoparticles (1 mg/mL) in distilled water was observed via transmission electron microscopy .Ex = 400 nm/λEm = 505 nm.Mouse colorectal cancer (CT26) cells were purchased from the Korean Cell Line Bank . They were cultured in 10% FBS containing high-glucose DMEM medium and then collected by scraping, with treatment of cell lysis buffer, using the cathepsin B activity assay kit . The cell solution was centrifuged at 13,000 rpm for 15 min at 4 °C, followed by 5 min incubation on ice for the collected supernatants. After determining the protein concentration for the collected supernatant using a Pierce BCA protein assay kit (Thermo Fisher Scientific), 50 µg of cell lysate was combined with cathepsin B reaction buffer (cathepsin B activity assay kit) in a 96-well black plate. Cathepsin B inhibitor  was used as a control to inhibit cathepsin B activity in CT26 cells and for comparison with different treatments of RR–BA . In addition, RR peptide  was also used to compare the inhibition effect of cathepsin B activity with RR–BA. After the addition of a fluorogenic cathepsin B substrate, Ac-RR-AFC , the plate was incubated for 2 h at 37 °C. Then, the fluorescence of the wells was measured using a microplate reader (SpectraMax M2) at wavelengths of λThe molecular structure of RR–BA was drawn using ChemDraw Professional 20.1.1.125 . Molecular docking was performed with AMDock (Assisted Molecular Docking) software, version 1.5.2, using cathepsin B protein (PDB: 1QDQ)  and a CH4 cells/well (for 24 h) or 5.0 × 103 cells/well (for 48 h). First, they were preincubated at 37 °C in a 5% CO2 incubator for 1 h. Then, the cells were further incubated with 0.1–1000 µM of RR, BA, or RR–BA for 24 and 48 h. The treated wells (n = 6) were evaluated using an Ez Cytox cell viability assay kit  and a microplate reader  at wavelengths of 450/600 nm. The cell viability was calculated from the measured UV absorbance values of samples compared with that of the control blank.The cytotoxicity of the RR–BA conjugates was evaluated using a mouse-derived colon carcinoma cell line (CT26 cells). The CT26 cells were cultured with high-glucose Dulbecco’s Modified Eagle’s Medium (DMEM) containing 10% fetal bovine serum and 1% antibiotic/antimycotic solution. Cells were seeded in 96-well plates at a concentration of 1.0 × 106 cells/50 µL of CT26 cells were inoculated in the back of 6-week-old male BALB/c mice . After approximately 1 week, when the tumor volume had grown to approximately 50–80 mm3 , the tumor-bearing mice were divided into four groups. Then, the mice were intravenously (i.v.) injected or administered per os (p.o.) with normal saline, RR–BA , RR peptide , or BA  for 20 days. BA (ursodeoxycholic acid) was administered orally due to solubility problems. The body weight and tumor volume of the treated mice were measured every other day using a digital caliper . After 20 days of treatment, the tumors were extracted from the sacrificed mice and then fixed with 10% neutral buffered formalin . The isolated tumor tissues were stained with hematoxylin and eosin solution (H&E), and then sliced for histological observation using an inverted microscope . In addition, the isolated tumor tissues were evaluated via terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining. The result was quantified using Stardust (Fiji image processing) and ImageJ   and 366.9 [(M + 2H+)/2]) /2]) B. In add–8.5 ppm C.The nanoparticle structure of RR–BA in saline was characterized using dynamic light scattering (DLS). As expected from the amphiphilic property of RR–BA molecules, they formed self-assembled nanoparticles with a Z-average size of 283.6 nm (intensity) A or 286.To confirm the molecular binding of RR–BA with the cathepsin B protein, molecular docking was conducted to evaluate the potential of RR–BA as an inhibitor of cathepsin B. RR–BA was stably and well bound to the potential binding site of cathepsin B A compareA 100 ps molecular dynamics (MD) simulation with the bound molecule complex was performed to investigate the binding stability of RR–BA in the catalytic pocket of cathepsin B. The RR–BA and cathepsin B molecules were simulated after adding the effect of the implicit water molecule with cathepsin B. During a 100 ps MD simulation, the dissociation of the RR molecule was observed at 72 ps, while the RR–BA molecule maintained its position by forming a stable complex at the binding site of cathepsin B D. This iA cathepsin B activity assay was conducted to confirm RR–BA as a cathepsin B inhibitor. It was performed using CT26 tumor cells as cathepsin B donors, with Ac-RR-AFC  as a fluorogenic substrate as the control. A total of 1 mM of the cathepsin B inhibitor from a cathepsin B activity assay kit  was used as a positive control. Various concentrations  of RR peptide or RR–BA were used to compare cathepsin B inhibition with the cathepsin B inhibitor. As expected, RR–BA significantly inhibited the enzyme activity of cathepsin B as a cathepsin B inhibitor by binding to cathepsin B instead of Ac-RR-AFC . The flusTo evaluate the cathepsin-B-based cancer cell cytotoxicity of RR–BA nanoparticles, we carried out an in vitro cytotoxicity assay in CT26 colon carcinoma cells for 24 or 48 h. When the CT26 cells were given different concentrations of RR–BA (0.1–1000 µM) for 24 h, the RR–BA showed tumor cell cytotoxicity compared with the RR peptides and BA A. Furthe3, n = 6) exhibited significant tumor inhibition compared with those treated with BA , RR peptides , or the control group . After the tumor-bearing mice were sacrificed, the isolated tumor weight of the mice treated with RR–BA (537 ± 423 mg) was significantly lower than that of the BA (1306 ± 195 mg), RR peptide (1257 ± 266 mg), and untreated control (1634 ± 191 mg) groups (The self-assembling nature of RR–BA nanoparticles might improve their tumor targeting effect on tumor tissues via an enhanced permeability and retention (EPR) effect ,35,36. T) groups B. The re) groups C. In add) groups D, meaninh RR–BA 5 ± 423 mgFurthermore, an H&E staining analysis of the isolated tumors was conducted to confirm apoptosis and cell death in the tumor tissue. The results showed extensive destruction in the tumor tissue in the RR–BA-administered mice A. AdditiThe novel nano-sized RR–BA conjugates developed in this paper showed remarkable therapeutic potential and low toxicity. Cathepsin B is a lysosomal protease that is involved in tumor progression and is a promising therapeutic target for cancer. Inhibiting cathepsin B has the potential to slow down tumor growth. The development of this type of nano-sized cathepsin B inhibitor may be a promising approach for future anticancer therapy. In this study, we designed a new drug candidate based on the concept of peptide-drug conjugates and nanoformulation. The synthesized RR–BA molecules showed significant cathepsin B inhibitory ability and anticancer effects through cathepsin-B-specific peptides and hydrophobic BA. However, like other cathepsin B inhibitors, it has a limitation in that its anticancer effect is not strong enough to completely suppress the tumor, but its therapeutic efficacy as an anticancer drug candidate is sufficient. In addition, because of its great value as an oral agent, there are areas to be studied in the future. Therefore, RR–BA could be a new potential anticancer drug candidate for anticancer therapy. Based on these results, we conclude that RR–BA is a novel anticancer agent with cathepsin B inhibitory efficiency and low toxicity for biomedical applications.


The biological and pathological functions of cathepsinB occurin acidic lysosomes and at the neutral pH of cytosol, nuclei, andextracellular locations. Importantly, cathepsin B displays differentsubstrate cleavage properties at acidic pH compared to neutral pHconditions. It is, therefore, desirable to develop specific substratesfor cathepsin B that measure its activity over broad pH ranges. Currentsubstrates used to monitor cathepsin B activity consist of Z-Phe-Arg-AMCand Z-Arg-Arg-AMC, but they lack specificity since they are cleavedby other cysteine cathepsins. Furthermore, Z-Arg-Arg-AMC monitorscathepsin B activity at neutral pH and displays minimal activity atacidic pH. Therefore, the purpose of this study was to design andvalidate specific fluorogenic peptide substrates that can monitorcathepsin B activity over a broad pH range from acidic to neutralpH conditions. In-depth cleavage properties of cathepsin B were comparedto those of the cysteine cathepsins K, L, S, V, and X via multiplexsubstrate profiling by mass spectrometry at pH 4.6 and pH 7.2. Analysisof the cleavage preferences predicted the tripeptide Z-Nle-Lys-Arg-AMCas a preferred substrate for cathepsin B. Significantly, Z-Nle-Lys-Arg-AMCdisplayed the advantageous properties of measuring high cathepsinB specific activity over acidic to neutral pHs and was specificallycleaved by cathepsin B over the other cysteine cathepsins. Z-Nle-Lys-Arg-AMCspecifically monitored cathepsin B activity in neuronal and glialcells which were consistent with relative abundances of cathepsinB protein. These findings validate Z-Nle-Lys-Arg-AMC as a novel substratethat specifically monitors cathepsin B activity over a broad pH range. In addition to thesignificant cellular function of cathepsin B at the neutral pH ofthe cytosol, cathepsin B is also present at neutral pH cellular locationsof the nucleus38 as well as extracellular locations,especially in human diseases such as Alzheimer’s disease40 and cancer.43Cathepsin B is a lysosomal cysteine proteasethat participatesin protein degradation to maintain cellular balance of functionalprotein pathways.46 indicate the need tospecifically monitor cathepsin B activity over a broad pH range, withoutdetecting other cysteine cathepsins. Currently, no such substratesexist because Z-Phe-Arg-AMC that is routinely used to assay cathepsinB in the field also monitors cathepsin L and other cysteine proteaseactivities.50 Furthermore, Z-Arg-Arg-AMC has been found as a specific substratefor cathepsin B over other cysteine cathepsins,48 but this substrate preferentially monitors the neutral pH activityof cathepsin B rather than the enzyme’s acidic activity.44 Therefore, the purpose of this study was torationally design selective peptide-AMC substrates that specificallymonitor cathepsin B activity with high specific activity at both acidicand neutral pH conditions, without cleavage by other cysteine cathepsins.The prominent biological functions of cathepsinB at distinct acidicand neutral pH environments of tissues51 These substrate properties of cathepsin B werecompared to those of other cysteine cathepsins L, K, S, V, and X atacidic and neutral pH conditions. The unique cleavage properties ofcathepsin B allowed design of Z-peptide-AMC substrates for assessmentof broad pH monitoring of cathepsin B activity, with specificity shownby a lack of cleavage by other cysteine cathepsins.The strategy of this study was to design specific substrates forcathepsin B based on its unique pH-dependent cleavage properties atacidic pH 4.6 compared to those at neutral pH 7.2. Cleavage propertiesof cathepsin B were determined by multiplex substrate profiling massspectrometry (MSP-MS) that utilizes a defined peptide library designedto contain all known amino acids adjacent to protease cleavage sites.48 Furthermore, the novel Z-Nle-Lys-Arg-AMC substrate was demonstratedto specifically measure cathepsin B activity in neuronal and glialcells that contain varying levels of cathepsin B and other cathepsins(determined by proteomics assessment). Overall, the novel Z-Nle-Lys-Arg-AMCsubstrate advantageously monitors specific cathepsin B activity overa broad pH range of acidic to neutral pH conditions and, thus, willbe useful for the assessment of cathepsin B activity at physiologicalpH conditions.Resultsidentified Z-Nle-Lys-Arg-AMC that monitors high specificactivity of cathepsin B at acidic to neutral pHs, and this substrateis specific for cathepsin B over other cysteine cathepsins L, K, S,and V. Kinetic studies demonstrated greater catalytic efficiency ofZ-Nle-Lys-Arg-AMC for analysis of acidic and neutral pH cathepsinB activity compared to the Z-Arg-Arg-AMC substrate, currently usedin the field as a specific cathepsin B substrate.Cathepsin B and cysteine cathepsins , wereobtained from R&D Systems  or Abcam . Proteases obtained for this study were procathepsin B , active cathepsin L ,active cathepsin K , procathepsin S , procathepsin V , andprocathepsin X . Mouse cathepsin B was obtainedfrom R&D Systems . The UniProt identificationnumbers for the human cathepsin proteases studied are P07858 for cathepsinB, P07711 for cathepsin L, P43235 for cathepsin K, P25774 for cathepsinL, 060911 for cathepsin V, and Q9UBR2 for cathepsin X . The Uniprot identification for mouse cathepsin Bis P10605.The substrates Z-KR-AMC, Z-GKR-AMC, Z-VKR-AMC, Z-YKR-AMC,Z-FKR-AMC, Z-WKR-AMC, Z-LKR-AMC, Z-AKR-AMC, Z-nRR-AMC, and Z-nKR-AMC(“n” represents norleucine) were custom-synthesizedby GenScript . Z-RR-AMC was purchased from Bachem(#4004789) . Z-FR-AMC was purchased from Anaspec (#AS-24096).The design and synthesis of the 228 14-mer peptidelibrary usedfor MSP-MS assays are described in ref . MSP-MS Cell culture of human neuroblastoma (SHSY-5Y and SK-N-MC)and mousemicroglia (BV2) utilized reagents consisting of RPMI 1640, MEMa, F-12K,heat-inactivated fetal bovine serum, and phosphate-buffered saline(PBS) . For proteomics,cells were homogenized in a buffer containing a cocktail of proteaseinhibitors of pepstatin A, leupeptin, chymostatin, and AEBSF , and E64c .Proteomics of cells utilized trypsin/Lys-C , Empore C18 (octadecyl) , and peptideassay  for sample preparation, combinedwith nano-LC–MS/MS reagents of trifluoroacetic acid, formicacid, Optima LC–MS water, Optima LC–MS acetonitrile. LC columns utilized were a pricofrit self-pack columnof 360 μm OD, 75 μm ID, and 15 μm tip  and an Acquity UPLC BEH C18  .Recombinant human procathepsins B, V, and S were activated by incubationat 37 °C for 30 min in the 20 mM Na-acetate pH 5.5, 100 mM NaCl,5 mM DTT, 1 mM EDTA activation buffer. Recombinant human procathepsinX was activated by incubation at room temperature for 5 min in the20 mM citrate phosphate pH 3.5, 100 mM NaCl, and 5 mM DTT activationbuffer.Cathepsin B (0.04 ng/μL) activitywas assessed with Z-Arg-Arg-AMC and Z-Phe-Arg-AMC substrates  in 40 mM citrate phosphate buffer pH 4.6 or Tris–HClpH 7.2 in 1 mM EDTA, 100 mM NaCl, and 5 mM DTT with incubation atroom temperature (25 °C) in triplicate, and AMC fluorescencereadings per second (RFU/s)  wererecorded over a period of 30 min by a BioTek Synergy HTX plate reader(Software version 3.08.01). Enzyme initial velocity was calculatedusing the highest slope recorded for 10 consecutive fluorescent readingswithin the initial 30 min. The mean and standard deviation (SD) weredetermined from triplicates. RFU/s were converted to pmol/min/μg(specific activity) using AMC standard curves. All data were plotted,calculated, and analyzed using GraphPad Prism (version 9.4.1).51 Cathepsin X was performed only at pH 4.6. In a total volume of 22μL, cathepsin B (0.1 ng/μL), cathepsin L (0.04 ng/μL),cathepsin K (0.07 ng/μL), cathepsin S (1.2 ng/μL), cathepsinV (0.16 ng/μL), and cathepsin X (0.09 ng/μL) were incubatedwith a mixture of 228 14-mer peptides (0.5 μM for each peptide)in 50 mM citrate phosphate at pH 4.6 or pH 7.2, 100 mM NaCl, 5 mMDTT, 1 mM EDTA assay buffer for 15 and 60 min at 25 °C. 10 μLwas removed at 15 and 60 min to be combined with 60 μL of 8M urea. A control assay used inactivated cathepsins by incubatingit with 8 M urea for 60 min at 25 °C, prior to addition of thepeptide library in the assay buffer. Assays were conducted in quadruplicate.Samples were acidified by addition of 40 μL of 2% TFA and desaltedusing custom-made C18 spin tips. The collected liquid samples weredried completely in a vacuum centrifuge and then stored at −70°C. For LC–MS/MS analysis, dried samples were resuspendedin 40 μL of 0.1% TFA, and 4 μL was injected into the ThermoFisher Scientific Q-Exactive mass spectrometer with an Ultimate 3000HPLC. Peptides were separated by reverse-phase chromatography usinga C18 column at (65 °C), which was done as we have previouslydescribed.51MSP-MSwas performed for cathepsins B, L, K, S, and V at pH 4.6 and 7.2 bymethods that we have described previously.52 Outliers from groups of replicates were removedby Dixon’s Q test.53 Missing andzero values were treated the same and replaced with imputed valuesdetermined from randomized normally distributed numbers within therange of the smallest 5% dataset ± SD. The control 0 min valuesin MSP-MS obtained for cathepsins B, L, K, S, V, and X at pH 4.6 werealso analyzed by PEAKS (for n = 20). To determineif the peptide intensity data in at least one of the groups of the0, 15, and 60 min timepoints were statistically significant, ANOVAtesting with multiple testing correction was used; those with q < 0.05 were considered for further analysis. Cleavedpeptide products at the 15 or 60 min timepoints were defined as havingintensity scores of 8-fold or more with p < 0.05by the two-tailed homoscedastic t-test.PEAKS (v 8.5) software(Bioinformatics Solutions Inc.) was used for MS/MS data analysis.The 228 14-mer library sequence was used as the database for MS datasearching and matching, and as a control it was also searched witha decoy consisting of the 228 14-mer library sequences in a reverseorder. A precursor tolerance of 0.01 Da and 20 ppm for MS2 fragmentswas used. Data were filtered to 0.9% peptide sequence false discoveryrates determined from hits from decoys. Label-free quantification(LFQ) was used for peptide quantification, and the data was normalizedby LOESS-G normalization method using Normalyzer tool version 1.1.1.z-scores were calculated by the equation (X – μ)/σ, where X is the frequencyof the amino acid occurring in the “experimental data set”at a specific position relative to the cleavage site , μ is the frequency of the amino acid at a specificposition in the “reference data set”, and σ isthe SD. z-scores were utilized to generate iceLogoillustrations of the relative frequencies of amino acid residues ateach of the P3 to P1 positions of the cleaved peptides where heightsof the single letter amino acids represent “percent difference”,defined as the difference in the frequency for an amino acid appearingin the “experimental data set” relative to the “referencedata set”. Amino acids shown above the midline have positive z-scores, indicating preferred amino acids, and amino acidsshown below the midline have negative z-scores, indicatingnot preferred amino acids, illustrated using p <0.30 cutoff criteria in the iceLogo software with p < 0.05 labeled in purple.Evaluation of thefrequencies of the P4 to P4′ amino acids adjacent to the cleavagesites was conducted using the iceLogo software 1.3.8, where the “experimentaldata set” consists of the detected cleavage sites defined earlierand the “reference data set” that consists of all possiblecleavages within the MSP-MS library of 228 14-mer peptides. For comparison of cathepsinB activity at pH 4.6, pH 5.5, and pH 7.2 with multiple fluorogenicsubstrates, cathepsin B (0.04 ng/μL) was incubated with 40 μMsubstrates consisting of Z-Lys-Arg-AMC, Z-Phe-Arg-AMC, Z-Arg-Arg-AMC,Z-Gly-Lys-Arg-AMC, Z-Val-Lys-Arg-AMC, Z-Tyr-Lys-Arg-AMC, Z-Phe-Lys-Arg-AMC,Z-Trp-Lys-Arg-AMC, Z-Leu-Lys-Arg-AMC, Z-Ala-Lys-Arg-AMC, Z-Nle-Arg-Arg-AMC,and Z-Nle-Lys-Arg-AMC. AMC product formation (RFU/s) monitored in40 mM citrate phosphate, pH 4.6 and pH 7.2, 1 mM EDTA, 100 mM NaCl,and 5 mM DTT was determined from the average of RFU/s triplicatesat each pH condition.For analysis of pH-dependent activityof cathepsin B (0.04 ng/μL), proteolytic activity was monitoredover the pH range of pH 2.2–9.0 in 40 mM citrate phosphate(pH 2.2 to pH 6.6) and 40 mM Tris–HCl (pH 7.0 to 9.0), 1 mMEDTA, 100 mM NaCl, and 5 mM DTT, with preincubation in each pH bufferfor 10 min prior to initiating the assay by addition of Z-Nle-Lys-Arg-AMC,Z-Arg-Arg-AMC, or Z-Phe-Arg-AMC to a final concentration of 40 μMto generate the pH curve data.For evaluation of cathepsin Band cysteine cathepsins for substrateselectivity, each cathepsin was incubated with 40 μM substratesconsisting of Z-Phe-Arg-AMC, Z-Arg-Arg-AMC, and Z-Nle-Lys-Arg-AMC,and the activity was monitored in 40 mM citrate phosphate , 1 mM EDTA, 100 mM NaCl, and 5 mM DTT for cathepsinB (0.04 ng/μL), cathepsin K (0.03 ng/μL), cathepsin L(0.03 ng/μL), cathepsin S (0.14 ng/μL), cathepsin V (0.04ng/μL), and cathepsin X (0.20 ng/μL). Since cathepsinX cannot cleave any of the AMC substrates, MCA-RPPGFSAFK(Dnp)-OH (R&DSystems #ES005) was used to verify that cathepsin X was active.kcat and Km for Z-Nle-Lys-Arg-AMC,Z-Arg-Arg-AMC, and Z-Phe-Arg-AMC substrates were determined at pH4.6 and pH 7.2, using substrate concentrations of 225–5.9 μMwith 0.04 ng/μL cathepsin B. RFU values were converted to s–1 using AMC standard curves. The kcat and Km values were obtainedfrom curve fitting the converted RFU data using GraphPad Prism9 softwareto the equation v0 = (Vmax[S])/(Km + [S]), where v0 is the initial velocity of the enzyme withits corresponding [S] substrate concentration and Vmax is the maximum enzyme velocity at saturated [S]. Vmax = kcat[E]T, where [E]T is the total cathepsin B concentrationused in the assay. Km is the x-axis value [S], where y = Vmax/2. SD values for kcat and Km were determined from curving fitting the v0 and [S] data from triplicates.The kinetic parameters of 54 and SK-N-MC55 cells and embryonic mouse microglia BV2  were grown in 5% CO2 at 37 °Cunder sterile conditions. Growth media conditions were 10% heat-inactivatedfetal bovine serum  and media consistingof 90% MEMa (Life Technologies) for SK-N-MC cells, 45% Ham’sF12-K , and 45% MEMa (Life Technologies) for SH-SY5Yand 90% RPMI (Life Technologies) for BV2.Human neuroblastomaSH-SY5YCharacterization of the Z-Nle-Lys-Arg-AMC substrate in proteolyticactivity assays for specific cathepsin B activity of cell homogenates(prepared in 0.32 M sucrose) was conducted at 40 mM citrate phosphatepH 4.6, 5.5, and 7.2, 5 mM DTT, 1 mM EDTA, 100 mM NaCl, 1.2% DMSO,and homogenates of human neuroblastoma cell lines, SHSY-5Y and SK-N-MC,and mouse microglia cell line, BV2. A concentration of 60 μMZ-Nle-Lys-Arg-AMC, Z-Arg-Arg-AMC, and Z-Phe-Arg-AMC substrates wasused in the proteolytic activity assays. Cell homogenates were diluted1:10 prior to addition to assays. Homogenate protein concentrationswere measured by the Biorad protein assay kit . The cathepsin B specific inhibitor, CA-074, was used to specificallyinhibit cathepsin B activity at concentrations of 1 μM at pH4.6 and 5.5 and 10 μM at pH 7.2. Assays were performed in 96-wellplates at room temperature (25 °C) in a total volume of 100 μLwith triplicates. Cell homogenates were incubated with CA-074 or inhibitorcontrols at RT for 30 min in the absence of substrate, followed byaddition of the substrate and incubation at 37 °C. Fluorescencewas quantified at 15, 30, and 60 min after substrate was added, bya Biotek Synergy HTX microplate reader with excitation at 360 nm,emission at 460 nm, gain 50, top optics, and read height at 1 mm.Prism GraphPad software was used to analyze data.57 To summarize, approximately108 cells were collected from 90% confluent flasks withPBS washing and centrifugation at 500g. Cell pelletswere lysed by dounce homogenization in ice-cold 100 mM Tris pH 7.4,50 mM NaCl, 1 mM EDTA, and protease inhibitors . Tryptic digests for proteomics analyses were prepared from 200mg of total cellular protein as described previously,59 with two biological flask replicates per cell line andtriplicate technical replicate injections per biological replicate.Tryptic peptides were diluted to 0.5 mg/mL in 2% acetonitrile, 0.1%trifluoroacetic acid, and 2 μg total amount injected by nano-LC–MS/MSon a Dionex UltiMate 3000 nano LC and Q-Exactive mass spectrometer(Thermo Fisher Scientific). Spectra were acquired as described previously.59 For protein identification and quantitation,spectra were queried against a custom database consisting of the proteinsequences of 15 human cathepsins (SH-SY5Y and SK-N-MC) or 15 mousecathepsins (BV2) with PEAKS v. 8.5 using decoy-fusion and LFQ methods, with search parametersdescribed previously.59 Proteins were consideredidentified if present in at least 2 of 3 technical replicates andat least 1 biological replicate. Proteins were considered quantifiablebased on the same criteria as for identification. Peak intensity areasof quantifiable data are expressed as mean ± SD.Analysis of neuronal andmicroglial cells by proteomics MS utilized protocols that we havepreviously described elsewhere.50 Significantly, these findingsconfirm the lack of specificity of Z-Phe-Arg-AMC and Z-Arg-Arg-AMCsubstrates for cathepsin B.We compared the specific activity of human cathepsin B with thefluorogenic substrates Z-Phe-Arg-AMC and Z-Arg-Arg-AMC with that ofother cysteine cathepsins 1. Both oComparison of Z-Arg-Arg-AMC cleavageby cathepsin B at acidic lysosomal pH 4.6 and at neutral pH 7.2 ofthe cytosol showed that this substrate displays pH-dependent cleavageproperties. Z-Arg-Arg-AMC is readily cleaved by cathepsin B at neutralpH, but this substrate has poor cleavage activity at acidic pH thatis one-third of the specific activity at neutral pH.The lackof specificity of Z-Phe-Arg-AMC and Z-Arg-Arg-AMC forcathepsin B compared to other cysteine cathepsins, and preferenceof Z-Arg-Arg-AMC to monitor neutral pH cathepsin B, indicates theneed for the development of specific substrates of cathepsin B operatingover a broad pH range.51 These cysteine proteases cleaved peptides at both pH 4.6 and pH7.2 in the MSP-MS study, with the exception of cathepsin X that generateda low number of cleavage products at only pH 4.6 .The substratecleavage properties of human cathepsin B were compared to that ofthe cysteine cathepsins K, L, V, S, and X by MSP-MS analysis. MSP-MSwas conducted by incubation of each protease with a library of 22814-mer peptides at acidic pH 4.6 and neutral pH 7.2, and cleavageproducts were identified and analyzed using LC–MS/MS. The peptidelibrary used for MSP-MS analysis was designed to contain all knownprotease cleavage sites with respect to amino acid residues at cleavagesites combined with neighboring residues.44 These DPCP and endopeptidaseactivities of cathepsin B distinguish it from the cathepsins K, L,S, and V, which displayed endopeptidase activities and no carboxypeptidaseactivity  cleavages occurring betweenresidues 12 and 13 of the peptide substrates, as well as lower endopeptidasecleavages occurring within the 14-mer substrates 1a. The dactivity 1b–e.Catactivity 1f. Heat mapsof z-scores illustrated the preferred residues (greenshade) and non-preferred residues (yellow shade) at each of the P4to P4′ positions. All amino acids were assessed with the exceptionof methionine and cysteine; norleucine (n) was used as a sulfur-freeisostere of methionine.The substrate cleavage specificity of cathepsinB was comparedto that of the other cysteine cathepsins by assessing preferred residuesat the P4–P4′ positions surrounding the P1–P1′cleavage site using Figures S2 and S3). We focused on the preferredresidues at positions P3 to P1 for cathepsin B . Z-Phe-Arg-AMCdisplayed high kcat/Km values similar to Z-Nle-Lys-Arg at pH 7.2 and had highercatalytic efficiency at pH 4.6.The pH properties of Z-Nle-Lys-Arg-AMC wereassessed and compared to the established substrates Z-Arg-Arg-AMCand Z-Phe-Arg-AMC 4. Z-Nle-Figure S4). In contrast, Z-Arg-Arg-AMC was not specific forcathepsin B since this substrate was cleaved by cathepsins L and V.The Z-Phe-Arg-AMC substrate was cleaved by cathepsin L with greateractivity than that for cathepsin B; this substrate was also cleavedby cathepsins K and V. These data demonstrate Z-Nle-Lys-Arg-AMC asa specific substrate for cathepsin B.Z-Nle-Lys-Arg-AMC specificallydetected cathepsin B activity and not cathepsins L, K, S, V, and Xactivities evaluated at pHs 4.6, 5.5, and 7.2 . Cathepsin B is present in situ withinthe pH 5.5 environment of secretory vesicles that produce peptideneurotransmitters.60 The two neuroblastomacell lines showed similar specific activities of cathepsin B. Themicroglial cells displayed high cathepsin B specific activity thatwas 2 to 4 times higher than that of the neuroblastoma cells dependingon the pH. Notably, the Z-Arg-Arg-AMC substrate detected none or littleactivity at pH 4.6 in the three cell types, whereas the Z-Nle-Lys-Arg-AMCsubstrate detected robust cathepsin B activity.To evaluate the novelsubstrate Z-Nle-Lys-Arg-AMC in a complex biological sample that containsother proteases and proteins, the assay of cathepsin B activity inhomogenates of human neuroblastoma cells (SHSY-5Y and SK-N-MC neuroblastoma)and mouse microglial cells (BV2 microglia) compared the use of theZ-Nle-Lys-Arg-AMC substrate to that of Z-Arg-Arg-AMC and Z-Phe-Arg-AMCsubstrates. Cathepsin B activity was assessed as CA-074 sensitiveactivity 6,ensuriFigure S6). The highest specific activity ofmouse cathepsin B was observed with Z-Nle-Lys-Arg-AMC among the threesubstrates. Z-Arg-Arg-AMC-cleaving activity of cathepsin B was low.A moderate specific activity was observed with Z-Phe-Arg-AMC. Theseactivities of mouse cathepsin B with the three substrates were representedby kcat/Km kinetic values, showing high catalytic efficiency for the Z-Nle-Lys-Arg-AMCsubstrate, modest catalytic efficiency for Z-Phe-Arg-AMC, and lowcatalytic efficiency for the Z-Arg-Arg-AMC substrate . These findings demonstrate Z-Nle-Lys-Arg-AMCas an excellent substrate for mouse cathepsin B as well as for humancathepsin B.To assess the prediction that mouse cathepsin Bresembles humancathepsin B for substrate preferences as demonstrated by the cellularassays, purified recombinant mouse cathepsin B was evaluated withthe Z-Nle-Lys-Arg-AMC and Z-Arg-Arg-AMC substrates, as well as theZ-Phe-Arg-AMC substrate at pHs 4.6, 5.5, and 7.2 (Supporting Information To assess whether the higher cathepsin B specific activity in microgliacompared to that in neuroblastoma cells may be related to the greaterlevels of cathepsin B protein, the relative levels of cathepsin Bprotein in these cells were evaluated by proteomics MS 2. Label-It is interesting to observe that the microglial cellscontainedhigher levels of multiple cysteine cathepsins compared to the neuroblastomacells. The microglial cells contained robust levels of cathepsinsB, D, and X, along with cathepsins A, K, L, and C. In the SH-SY5Yneuroblastoma cells, only two cathepsins, B and D, were detected;in the SKNMC neuroblastoma cells, cathepsins B and D were abundantand low levels of cathepsin C were detected.These findingsillustrate the cell type-specific differences inthe expression levels of cathepsin B and related cysteine cathepsins.Notably, our newly developed substrate, Z-Nle-Lys-Arg-AMC, effectivelydetects the relative levels of cathepsin B activity. These resultscontribute to defining the variability of cathepsin B activity amongdifferent cell types and highlight the utility of Z-Nle-Lys-Arg-AMCas a tool for assessing cathepsin B specific activity in biologicalsystems.47 It is, therefore, necessary to develop specific substrates for cathepsinB that specifically measure its proteolytic activity over broad pHranges. Evaluation by this study of current substrates used to monitorcathepsin B activity, consisting of Z-Phe-Arg-AMC and Z-Arg-Arg-AMC,found that they lack specificity since they are cleaved by other cysteinecathepsins; furthermore, Z-Arg-Arg-AMC does not optimally assess cathepsinB activity at acidic pH. Therefore, the purpose of this study wasto conduct in-depth substrate cleavage profiles of cathepsin B comparedto other cysteine cathepsins K, L, S, V, and X for designing specificfluorogenic substrates that can monitor cathepsin B proteolytic activityat acidic to neutral pH conditions. Cleavage profiles of these proteaseswere assessed by MSP-MS that analyzed cleavages of peptide librarycomponents at pH 4.6 and pH 7.2. Analysis of the preferred and non-preferredresidues at the P3, P2, and P1 positions adjacent to the P1–P1′cleavage sites predicted the tripeptide Z-Nle-Lys-Arg-AMC as a preferredsubstrate for cathepsin B. Significantly, Z-Nle-Lys-Arg-AMC displayedthe advantageous properties of measuring high cathepsin B specificactivity over acidic to neutral pHs and was specific for cathepsinB over other cysteine cathepsins. Z-Nle-Lys-Arg-AMC specifically measuredcathepsin B activity in neuronal and glial cells which were consistentwith relative abundances of cathepsin B protein. These findings validateZ-Nle-Lys-Arg-AMC as a novel substrate that specifically monitorscathepsin B activity over a broad pH range.The prominent functions of cathepsin B inhealth and disease occurin acidic lysosomes and at the neutral pH of cytosol, nuclei, andextracellular locations. Notably, cathepsin B displays different substratecleavage properties at the acidic pH compared to neutral pH conditions.Substrate cleavageprofiling analysis of cathepsin B and cysteinecathepsins conducted by MSP-MS, at pH 4.6 and pH 7.2, revealed eachprotease’s distinct pattern of preferred amino acids at P4to P4′ positions of the P1–P1′ cleavage sitesof the peptide library. We focused on the P3 to P1 residue preferencesfor designing Z-peptide-AMC substrates that utilize non-prime siteresidues of cleavage sites. Comparisons showed that cathepsin B displayedunique preferences over other cysteine cathepsins, consisting of Nleat the P3 position, Lys at the P2 position, and Arg at the P3 positionat both acidic and neutral pHs. This evaluation formed the basis forthe design of Z-Nle-Lys-Arg-AMC as a specific cathepsin B substrate.Indeed, the assessment of Z-Nle-Lys-Arg-AMC with a series of Z-peptide-AMCsubstrates indicated that Nle was important over other substitutionsconsisting of Leu, Tyr, Phe, Ala, Trp, Gly, or Val. Dipeptides containingdibasic sequence combinations of Lys and Arg were poor compared toZ-Nle-Lys-Arg-AMC as the substrate. The pH profile of Z-Nle-Lys-Arg-AMCdetected cathepsin B of high specific activity at pH 4.5 to pH 7.5,which covers the biological pH range of cathepsin B within acidiclysosomes to modestly acidic secretory vesicles and the neutral cytosol,nuclei, and extracellular locations.The high catalytic activityand specificity of Z-Nle-Lys-Arg-AMCwere based on the preference of cathepsin B for P3 and P2 residuesadjacent to the P1–P1′ cleavage site. When comparedto the Z-dipeptide-AMC substrates, the Z-tripeptide-AMC variants withinclusion of Nle as the P3 residue enhances cathepsin B specificityand catalytic efficiency, as shown by the higher specific activitiesfor Z-Nle-Lys-Arg-AMC and Z-Nle-Arg-Arg-AMC compared to their dipeptidevariants Z-Lys-Arg-AMC and Z-Arg-Arg-AMC, respectively. Notably, Valor Gly at P3 prevented any turnover of the substrate, suggesting thatthe P3 residue interacting with the enzyme S3 pocket plays a key rolein substrate binding even when favorable amino acids are present atother positions. Additionally, at the P2 position, cathepsin B preferredLys and Arg basic residues residues at both acidic pH 4.6 and neutralpH 7.2.53 indicating CA-074-sensitiveactivity. While Z-Nle-Lys-Arg-AMC was highly sensitive for monitoringcathepsin B activity, Z-Arg-Arg-AMC could monitor only low cathepsinB activity. The Z-Phe-Arg-AMC substrate detected cathepsin B as wellas other cysteine cathepsin activity and, thus, was not specific forcathepsin B.In neuronal and glial cells, cathepsin B activity wasmeasuredwith high specific activity with the novel substrate Z-Nle-Lys-Arg-AMC.Cathepsin B activity was confirmed by inhibition by CA-074, a specificinhibitor of cathepsin B,43 where cathepsin B activity can be monitored by Z-Nle-Lys-Arg-AMC.Furthermore, bacterial infection promoted cellular cathepsin B activityat a neutral pH environment61 and, thus,the Z-Nle-Lys-Arg-AMC can be valuable for monitoring cathepsin B atneutral pH cellular environments in infectious diseases. The sensitivityof this substrate is particularly advantageous for detecting low concentrationsof cathepsin B in diverse biological and pathological systems thatwould otherwise not be detected with the traditionally used substrates.Therefore, use of this substrate may enable a more comprehensive understandingof the role of cathepsin B in human diseases and biological systems.Z-Nle-Lys-Arg-AMC holds promise for evaluationof cathepsin B invarious cellular and disease conditions. For example, cathepsin Bis secreted by cancer cells into the tumor microenvironmentFindings of this study show that the novel Z-Nle-Lys-Arg-AMC substrateis specific for cathepsin B over other cysteine cathepsins and isa sensitive substrate to monitor cathepsin B activity over a broadpH range from acidic to neutral conditions. This substrate is advantageousover other routinely used substrates for cathepsin B which are non-specificor unable to detect the enzyme’s activity at low to high pHconditions. Z-Nle-Lys-Arg-AMC detects cathepsin B in human and mousespecies cell types and thus will be useful for clinical cathepsinB biomarker studies as well as for mouse models of human disease conditions.


The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults.n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data.We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training , far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects.Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely. Age-related hearing loss (ARHL) is a common condition that occurs naturally as individuals age and is characterized by progressive bilateral high-frequency sensorineural hearing loss. As life expectancy increases globally, ARHL has become a significant health concern, particularly for individuals aged 60 and above in both the United States and Taiwan , 2. ThisWhile pure tone audiometry remains the gold standard for detecting hearing loss, its feasibility in large-scale population-based settings may be limited. As a result, there is growing interest in alternative screening techniques that are more accessible and efficient. Recent research has explored various options, such as whisper voice testing , telephoDeclining attention span in older individuals can impact their ability to complete lengthy questionnaires, making it essential to develop a shorter and more efficient screening tool for hearing loss . AdditioThe primary objective of this study is to develop a concise version of the HHIE-S questionnaire and evaluate its effectiveness in a Taipei community using a machine learning algorithm . Integrap > 0.05) were included in the study.A government-funded annual geriatric health check-up program was available to Taipei residents 65 years of age and older from January 2016 to December 2018. Notified by the city government, eligible participants usually had examinations at neighborhood community hospitals. Participants from Taipei City Hospital's Heping branch, which serves the Zhongzheng and Wanhua districts of Taipei, were the study's primary focus. A total of 1,526 adults  questionnaire in Traditional Chinese face-to-face, and an audiologist or an undergraduate student studying audiology under supervision recorded the results in their medical records. There were twelve features in the dataset that were analyzed, including demographic variables like gender and age. The TC-HHIE-S questionnaire, comprising ten questions, was also included, offering a thorough understanding of the participants' hearing health.As the gold standard for detecting hearing loss, pure-tone audiometry was carefully carried out in a sound-treated booth in our study, with ambient noise levels strictly kept below 30 dBA. To perform the audiometry tests, we used the MA30 Audiometer  in conjunction with the TDH-39 supra-aural earphones. Four key frequencies—0.5 kHz, 1 kHz, 2 kHz, and 4 kHz—were carefully measured to determine the air conduction pure-tone thresholds. These measurements were primarily taken in the better-hearing ear, with the right ear being used as a default when there was no discernible hearing difference between the two ears. These thresholds were then averaged in the better-hearing ear to determine the pure-tone average (PTA). Narrow band masking was used as needed to guarantee the precision of our measurements, and participants usually indicated their hearing responses with a standard patient response button. An essential component of our approach involved our equipment's yearly calibration, which was conducted with strict adherence to ISO 389–1 and 389–3 guidelines. This step was essential to maintaining the integrity and robustness of our study's findings by ensuring the accuracy and consistency of our audiometric data.To account for varying degrees of hearing loss, we used a straightforward and methodical method to divide the Pure-Tone Average (PTA) results of study participants into two groups. The established hearing threshold cutoff points served as the basis for this classification. More specifically, individuals with a PTA of less than 40 dB HL—a sign of mild to moderate hearing loss—were given a classification value of 1. In contrast, individuals whose PTA matched or exceeded 40 dB HL were assigned a classification value of 0. The latter category represents hearing loss ranging from severe to profound. We were able to classify the study participants' hearing impairment severity with this binary classification system, which gave us a solid foundation for our machine learning analysis later on.n = 1220) and identify the best data transformations, algorithms, and parameter settings for our predictive modeling task [n = 306). To improve model efficiency, we implemented forward feature selection on the training data to identify the most informative subset of features. By developing and evaluating models solely on the training set, we prevented leakage and overfitting to the test data.For model development, we utilized the graphical tool Auto AI in Watson Studio to analyze the 80% training data n = 10 and ideing task . The Auting task –27. We aing task –30, grading task –33, snaping task , 34, 35,ing task –38, snaping task , 27, 39,ing task –42 on thTo optimize the performance of the machine learning models in predicting ARHL, we employed a grid search combined with fivefold cross-validation for XGBoost, GBC, SDTC, LGBM, SRFC, and LR –46. DuriWe compared the models' Area Under the Receiver Operating Characteristic Curve (AUC) values to assess the machine learning models' discriminative performance. Because it indicates how well a model can distinguish between classes, the AUC metric is important. We used a suite of performance metrics to conduct a thorough assessment of the models' efficacy on the testing dataset, in addition to AUC. These included the F1 score, which strikes a balance between recall and precision; accuracy, which gauges the model's overall correctness; precision, which shows the percentage of true positive identifications that were correctly identified; recall, which gauges the percentage of true positives that were correctly identified; average precision, which provides a summary of a precision-recall curve; and log loss, which assesses the prediction error of the models. By utilizing this diverse range of metrics, we were able to perform a comprehensive evaluation of the models' efficacy, guaranteeing a detailed and refined comprehension of their potential to forecast age-related hearing loss (ARHL).We used SHapley Additive exPlanations (SHAP) analysis to clarify the underlying mechanisms guiding the predictions of our model and to provide guidance for bettering hearing screening procedures. A state-of-the-art approach in explainable AI called SHAP offers a detailed perspective of how each feature influences the model's predictions. Our main objective was to analyze the importance of different elements in the HHIE-S survey. We were able to identify the most important questionnaire items and comprehend their individual effects on the risk prediction of age-related hearing loss (ARHL) by utilizing SHAP values. Through the identification of important ARHL risk factors in our dataset, this analysis was able to offer valuable insights for the development of both our predictive model and the field of hearing health assessment as a whole. It enabled us to see the direction and strength of each feature's influence on the model's output in addition to quantifying each feature's importance.The conclusions drawn from our SHAP analysis have a major impact on how focused interventions for those at risk of hearing loss are developed. Our machine learning model identifies the major factors that influence the risk of age-related hearing loss (ARHL), and this approach opens the door to more customized and efficient hearing care management plans. Comprehending these crucial elements enables medical practitioners to devise interventions that are tailored to each individual's specific requirements and risk profile, while also being responsive to the broad patterns noted in ARHL. This tailored approach is anticipated to substantially improve the effectiveness of interventions, leading to enhanced outcomes in terms of both health and quality of life for older adults afflicted with ARHL. The application of such data-driven, personalized healthcare strategies marks a significant advancement in the field of audiology and geriatric care, promising a more nuanced and impactful approach to managing and mitigating the effects of hearing loss.The open-source Scikit-learn library was heavily utilized in conjunction with Python, more especially Python Software Foundation version 3.9, as the main platform for all machine learning analyses in our study. A wide variety of tools and algorithms that were essential for our analysis were provided by this library. To effectively test our models on unseen data, we divided the dataset into training and testing sets at random using the sklearn.model_selection.train_test_split module. We used the XGBoost Python package, which is well-known for its effectiveness and high performance in gradient boosting, to build the XGBoost model. The ensemble of sklearn. Our Gradient Boosting Decision Tree (GBDT) model was developed with the help of the GradientBoostingClassifier module, which constructed an ensemble of weak learners to enable precise prediction-making. The sklearn.ensemble was also used by us. The Extra Trees model, an ensemble approach based on decision trees that provides robustness and feature importance estimation, was built using the ExtraTreesClassifier. The lightgbm. LGBMClassifier Python package, which is preferred for its effectiveness in large-scale applications, was used to develop the Light Gradient Boosting Machine (LGBM) model. A decision tree classifier and sklearn.linear_model were supplied by the SnapDecisionTreeClassifier algorithm from the IBM Snap ML library. The logistic regression model, which is frequently applied to binary classification issues, was utilized. Use sklearn.model_selection to avoid overfitting and guarantee robustness. For stratified k-fold cross-validation, Stratified K Fold was utilized. To ensure compliance with accepted scientific practices, we maintained a p-value of 0.05 to denote statistical significance throughout our analysis.The study received funding from a specific organization, with the crucial stipulation that the funders remained entirely uninvolved in any aspects of the research process. This encompassed study design, data collection, analysis, interpretation, writing, and paper submission. The clear separation between funding and research activities ensured that the research was conducted independently, without the potential for bias or influence from the funding source. All authors involved in the study had full access to the data, fostering transparency and unbiased research. The corresponding authors assumed final responsibility for paper submission, ensuring that the research adhered to scientific standards and ethical guidelines. They were accountable for upholding the accuracy and integrity of the work presented in the paper.In this work, we conducted a thorough analysis of a cohort of 1,526 older adults, which is representative of the larger population affected by age-related hearing loss (ARHL). To ensure a thorough evaluation of our machine learning models, this cohort was carefully split into two subsets: a primary training set of 1,221 participants, which was used to develop and refine the models, and a validation testing set of 305 participants, which was used to independently assess the models' performance and predictive accuracy.The study focused on an elderly population, which is most affected by ARHL, as evidenced by the average age of 74.6 years ± 2.2 years) across the entire cohort in terms of demographic characteristics. Upon closer inspection, we found that the testing set (72.7 years ± 0.6) and the training set (75.2 years ± 2.4) had slightly different average ages. These differences were considered throughout the analysis to guarantee the validity of our results. The cohort's gender distribution was reasonably uniform, with women making up roughly 53.7% of all participants. Table .2 years A nuanced comparison of HHIE-S scores across the full cohort, training, and testing sets showed subtle differences. For instance, the score for the HHIE-1 item was marginally higher in the full cohort (0.6 ± 1.2) than in the testing set (0.2 ± 0.7), while the training set maintained a score of 0.6 ± 1.2. Similar patterns were observed for the other HHIE-S items, underscoring the consistent representation of hearing impairment symptoms across subsets.Different performance characteristics were observed in our assessment of the predictive capabilities of different machine learning models, such as XGBoost, Gradient Boosting Classifier (GBC), Snap Decision Tree Classifier (SDTC), Light Gradient Boosting Machine (LGBM), and Snap Random Forest Classifier (SRFC). Remarkably, as shown in Fig. In this work, we employed SHapley Additive exPlanations (SHAP) values to determine the significance of each feature in the Light Gradient Boosting Machine (LGBM) model. As seen in Fig. For patients at risk of Age-Related Hearing Loss (ARHL), we used SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to provide comprehensive individual-level explanations of our machine learning (ML) model's predictions. The aforementioned interpretive tools, as illustrated in Fig. In this study, we utilized machine learning techniques to validate the efficacy of the HHIE-S tool in assessing the risk of ARHL , 48, 49.In our study, the LGBM model demonstrated the best specificity (0.88), showcasing its proficiency in accurately identifying true negative cases. Additionally, it exhibited the highest PPV of 0.92, indicating its capability to correctly predict positive cases with high confidence. The AUC analysis revealed that the LGBM model achieved the highest AUC of 0.83, signifying its superior overall discriminative ability. These results align with our previous findings, where PTA > 40 dB HL showed the highest sensitivity (76.9%) in hearing screening, indicating similar discriminatory power .The HHIE-S tool is a cost-effective and easily applicable assessment, making it suitable for diverse settings such as nursing homes, senior centers, primary care facilities, and community hearing screenings with numerous participants within a short timeframe. These locations often lack access to conventional hearing tests. This research emphasizes the significance of the HHIE-S in public health applications, as it can identify moderate and severe hearing loss in a considerable population. To further enhance the practicability and effectiveness of large-scale HHIE-S screenings, future studies should investigate the utilization of internet-based or smartphone-based methods for managing HHIE-S , 60.By employing LGBM models in our study, we observed enhanced predictive performance compared to that of traditional statistical methods. Notably, the LGBM achieved the highest AUC values, indicating its superior ability to predict ARHL risk. This result aligns with recent findings of the effectiveness of LGBM and other ensemble models in various medical applications. Conducting a feature importance analysis using SHAP values provided valuable insights into the most influential features . Age andSix features on the importance matrix plot and the SHAP summary plot of LGBM were age and the five HHIE-S results. The most important factor for the model output was "Do you feel that you have a disability because of a hearing problem?": Our model identified the 4th item of the HHIE-S questionnaire as the most influential in determining hearing loss. This question directly addresses the issue of hearing loss in a clear and understandable way. It measures emotional responses to hearing loss and is similar to the commonly used single question (SQ): "Do you have any difficulty with your hearing?", which has shown reasonable effectiveness in detecting hearing loss during large-scale screenings –65.Age is an important variable in detecting hearing loss in our model. Our study aligns with several studies that demonstrated age as a significant variable in questionnaires for detecting hearing loss . The sigOur analysis shows that the whisper test is the second most significant factor in measuring hearing loss. This is an interesting finding, as the whisper test is a simple and accurate screening method widely used in hearing screenings for elderly individuals, with reasonable sensitivity and specificity , 76–78. TV and radio are currently very common forms of media. However, a study has suggested that patients who report increased volume levels while watching TV may have an increased risk of hearing loss . It is pRestaurants are bustling environments filled with sounds such as background music, conversations, and the clanging of utensils, which can make it challenging for people with hearing loss to discern between sounds or comprehend speech. In fact, speech understanding difficulties in noisy environments are frequently cited as the top reason for consultations in audiology or hearing health services . It is iOur study adds to the expanding body of evidence supporting the integration of machine learning models into hearing screening protocols. By accurately identifying individuals at risk of ARHL, healthcare professionals can implement timely interventions, such as hearing aids or rehabilitation programs, to mitigate the impact of hearing loss on quality of life. The use of machine learning models enhances the efficiency and accuracy of screening processes, leading to improved patient outcomes. However, it is essential to acknowledge several limitations in our study. Focusing on a specific population of older adults limits the generalizability of our findings to other age groups or populations . Future Our study significantly extends the utility of the Hearing Handicap Inventory for the Elderly (HHIE) by integrating it with cutting-edge machine learning models. Traditionally, HHIE has been used primarily to assess the psychosocial impacts of hearing impairment through subjective experiences and self-perceived hearing difficulties. This novel method improves the precision and accuracy of Age-Related Hearing Loss (ARHL) predictions and introduces a more individualized approach to hearing healthcare by transforming HHIE from a simple self-reporting tool into an advanced predictive tool. We can customize interventions based on each patient's unique risk profile by utilizing machine learning, which combines quantitative, data-driven predictions with qualitative insights into patients' actual experiences. This combination marks a significant breakthrough in the field of hearing healthcare and the start of a new phase of proactive, patient-centered, precision-based care. Our approach provides a comprehensive and nuanced view of the management of hearing health by bridging the gap between subjective self-assessment and objective risk prediction. This adds quantitative and data-driven capabilities to the traditional HHIE approach, enabling a more holistic approach to hearing healthcare.Our research, which combines cutting-edge machine learning methods with the traditional Hearing Handicap Inventory for the Elderly-Screening (HHIE-S) tool, marks a major advancement in the assessment of hearing loss. In the field of audiology and hearing healthcare, the use of the Light Gradient Boosting Machine (LGBM) classifier in particular has shown impressive progress in improving predictive accuracy. In addition to demonstrating the enormous potential of data-driven approaches to supplement conventional diagnostic tools, this study paves the way for more prompt and precisely targeted interventions for age-related hearing loss (ARHL). The significance of adopting such novel approaches is highlighted by our study, as the healthcare sector continues to undergo swift transformation. Ensuring better patient outcomes and leading the way in the transition to a more proactive, precision-driven paradigm in hearing care depend on this. By utilizing machine learning, we create new opportunities for early detection and intervention, which will ultimately improve the quality of life for those who are impacted by hearing loss.


Borreliella burgdorferi. OspC is a helical-rich homodimer that interacts with tick salivary proteins, as well as components of the mammalian immune system. Several decades ago, it was shown that the OspC-specific monoclonal antibody, B5, was able to passively protect mice from experimental tick-transmitted infection by B. burgdorferi strain B31. However, B5’s epitope has never been elucidated, despite widespread interest in OspC as a possible Lyme disease vaccine antigen. Here, we report the crystal structure of B5 antigen-binding fragments (Fabs) in complex with recombinant OspC type A (OspCA). Each OspC monomer within the homodimer was bound by a single B5 Fab in a side-on orientation, with contact points along OspC’s α-helix 1 and α-helix 6, as well as interactions with the loop between α-helices 5 and 6. In addition, B5’s complementarity-determining region (CDR) H3 bridged the OspC-OspC′ homodimer interface, revealing the quaternary nature of the protective epitope. To provide insight into the molecular basis of B5 serotype specificity, we solved the crystal structures of recombinant OspC types B and K and compared them to OspCA. This study represents the first structure of a protective B cell epitope on OspC and will aid in the rational design of OspC-based vaccines and therapeutics for Lyme disease.Outer surface protein C (OspC) plays a pivotal role in mediating tick-to-host transmission and infectivity of the Lyme disease spirochete,  Borreliella burgdorferi, while in Eurasia LD is caused by related genospecies, including B. afzelii and B. garinii , or Lyme borreliosis (LB), is the most common tick-borne infection in North America and Europe. Clinical manifestations commonly associated with LD include erythema migrans, neuroborreliosis, carditis, and Lyme arthritis , 2. In N garinii , 4. In t factors –8.B. burgdorferi transmission and limit human infections  has long been considered as another candidate LD vaccine antigen. OspC is an ~23-kDa helical-rich lipoprotein expressed by ood meal –27. OspCsurvival , 28, 29.nfection , 31. Ind killing . OspC an disease , 33–37. sease 33–, 39. ThuB. burgdorferi genospecies. At least 25 different ospC alleles or OspC types (when referring to the protein) have been identified to date, with extensive diversity occurring even within B. burgdorferi isolates within limited geographical regions , I (OspCI) and E (OspCE), were solved more than 2 decades ago and shown to share a high degree of similarity  OspC type I [PDB ID 1F1M] colored yellow and magenta; (C) OspC type E [PDB ID 1G5Z] colored light blue and dark green. All alpha-helices (1-6) and beta-strands 1 and 2 are labeled highlighting structural similarity. Download FIG S1, PDF file, 0.3 MB.Structures of OspC types A, I and E. Ribbon diagrams of (A) OspC type A  fragmentation). Agilent MassHunter Qualitative Analysis with BioConfirm (version B.07.00) software was used for the analysis of all the mass spectrometry data. A total of 87 peptides were identified and mapped as shown in A sequence coverage with a median length of 17.0 residues and 8.6 average redundancy.For peptic peptide mapping, recombinant OspCy 50 mm) . DigesteA and B5 MAb were buffer exchanged and assayed using our previous protocol (A (19 μM) was thawed at room temperature. B5 sample was collated from 4°C and buffer exchanged on the day of experiment. Free the protein state (OspCA), sample was prepared by diluting 19 μM OspCA to 9 μM by the addition of 20 mM phosphate and 100 mM NaCl, pH 7.40. The bound state (OspCA +B5) was prepared by three strokes of mixing and adjusted to a final concentration of 9 μM. HX-MS labeling conditions, robot methods, maximally deuterated experiment (OspA paper) protocol, and data analysis were done as recently reported (OspCprotocol . Previoureported .B. burgdorferi OspCA (residues 38 to 201) was subcloned into the pSUMO expression vector that contained an N-terminal deca-histidine and SUMO tag. The PCR amplicons for B. burgdorferi OspCB and OspCK containing residues 38 to 202 were subcloned into the pMCSG7 expression vector that contained an N-terminal deca-histidine tag. Cloning was performed using standard ligase-independent cloning (LIC). All OspC types were expressed in Escherichia coli strain BL21(DE3). The transformed bacteria were grown at 37°C in TB medium and induced at 20°C at an optical density at 600 nm (OD600) of 0.6 with 0.1 mM isopropyl-β-d-thiogalactopyranoside (IPTG) for ~16 h. After induction, cells were harvested and resuspended in 20 mM HEPES (pH 7.5) and 150 mM NaCl. The cell suspension was sonicated and centrifuged at 30,000 × g for 30 min. After centrifugation, the protein-containing supernatant was purified by nickel-affinity and size exclusion chromatography on an AKTAxpress system , which consisted of a 1-mL nickel affinity column followed by a Superdex 200 16/60 gel filtration column. The elution buffer consisted of 0.5 M imidazole in binding buffer, and the gel filtration buffer consisted of 20 mM HEPES (pH 7.5), 150 mM NaCl, and 20 mM imidazole. Fractions containing each OspC type were pooled and subjected to tobacco etch virus (TEV) protease cleavage (1:10 weight ratio) for 3 h at room temperature to remove the respective fusion protein tags. The cleaved proteins were passed over a 1-mL Ni-NTA agarose (Qiagen) gravity column to remove TEV protease, cleaved residues, and uncleaved fusion protein. After purification, Fab B5 was complexed with OspCA in a 1:1 stoichiometry and then concentrated to 10 mg/mL final for all crystallization trials.The PCR amplicon encoding A complex were produced at 22°C using a crystallization solution containing 100 mM sodium cacodylate, pH 6.5, 5% polyethylene glycol (PEG) 8K, and 40% Hexylene glycol (MPD). Crystals of the OspCB were produced at 22°C using a crystallization solution containing 100 mM sodium phosphate citrate, pH 4.2; 41.9% PEG 600 crystals of the OspCK were produced at 4°C using a crystallization solution containing 100 mM Tris, pH 8.5, 40% PEG 400, 200 mM LiSO4, and 10 mM 2-aminoethanesulfonic acid. All crystals were flash-frozen in liquid nitrogen after a short soak in the appropriate crystallization buffers supplemented with 25% ethylene glycol. Data were collected at the 24-ID-E beamline at the Advanced Photon Source, Argonne National Labs. All data were indexed, merged, and scaled using HKL2000  along with the VL and CL domains of the human germ line antibody hepatitis E virus E2S antibody (PDB ID: 3RKD) as the search model for Fab B5 in the B5-OspCA complex. The OspC coordinates (PDB ID: 1GGQ) were used as the search model for OspCA in the OspCA-B5 complex. The same OspC coordinates (PDB ID: 1GGQ) were also used as search models for the OspCB and OspCK structure determinations. The resulting phase information from molecular replacement was used for some manual model building of each structure solved using the graphics program Coot (https://www.rcsb.org/) codes associated with each of the three structures generated in this study . Molecular graphics were prepared using PyMOL . All buried surface area calculations were done with PISA within the CCP4 suite (The B5 Fab-OspCg Phaser . Moleculram Coot  and struram Coot . Data coP4 suite .10.1128/mbio.02981-22.6FIG S6A, OspCB, and OspCK highlighting the key sequence similarities of OspCC3 and OspCI3 to OspCA. Red rectangles encapsulate critical residues in α-helix 1 and loop 5-6 highlighting the insertion at residue 74 and deletion at residue 165 in OspCB that antagonize B5 binding. Red asterisks above the sequence identify the critical OspCA residues 161 and 175, which support interaction with B5. Black asterisks below the sequence denote sequence identity, with two dots and one dot showing relatively reduced sequence similarity. Figure made with Clustal Omega. Download FIG S6, PDF file, 1.5 MB.Sequence alignment of OspC types that conceivably bind B5. Primary sequence alignment of OspC types C3 and I3 with OspCCopyright © 2023 Rudolph et al.2023Rudolph et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the