The purpose of this study was to introduce caffeic acid (CA) into electrospun nanofibers made of carob flour, whey protein concentrate (WPC), and polyethylene oxide (PEO). The effects of WPC concentration (1% and 3%) and CA additions (1% and 10%) on the characteristics of solutions and nanofibers were investigated. The viscosity and electrical conductivity of the solutions were examined to determine characteristics of solutions. Scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analyzer (TGA), differential scanning calorimetry (DSC), water vapor permeability (WVP), and Fourier transform infrared (FTIR) analysis were used to characterize the nanofibers. According to the SEM results, the inclusion of CA into nanofibers resulted in thinner nanofibers. All nanofibers exhibited uniform morphology. CA was efficiently loaded into nanofibers. When CA concentrations were 1% and 10%, loading efficiencies were 76.4% and 94%, respectively. Nanofibers containing 10% CA demonstrated 92.95% antioxidant activity. The results indicate that encapsulating CA into carob flour–WPC-based nanofibers via electrospinning is a suitable method for active packaging applications. Environmental factors such as pH, temperature, and light exposure, which can cause antioxidants to deteriorate, limit their bioavailability and bioaccessibility. Encapsulating antioxidants in biopolymer matrices is a successful approach to improve their bioavailability and stability . Nano- aTraditional food packaging provides mechanical support as well as protection against environmental hazards such as micro-organisms, moisture, oxygen, odors, and dust. On the other hand, active food packaging is designed to scavenge undesirable substances or to release preserving agents such as antioxidants or antimicrobial agents . There iCaffeic acid  is a phenylpropanoid and hydroxycinnamate metabolite found in plant tissues and food sources such as blueberries, apple cider, and coffee drink extracts. It is employed as a carcinogenic inhibitor which has antioxidant and antibacterial properties in vitro, hence aiding in the prevention of cardiovascular and atherosclerotic illnesses . To the Because of their good gas and liquid barrier properties, easy formability, availability, and low cost, petroleum-based synthetic films are used for food packaging. However, they are not biodegradable or sustainable, and they are also the source of significant disposal issues . There iCarob has a high fiber content (approximately 18%), mostly cellulose and hemicellulose, and also contains approximately 3–4% protein ,18. CellThe objective of this study was to encapsulate caffeic acid into biodegradable nanofibers made of carob flour, to be used as active packaging material. Since the protein ratio in carob is low, the addition of WPC was assumed to be more suitable for developing packaging materials in this study. A carob flour–whey protein concentrate blend has never been used as a nanofiber material before. The crystalline structure of the cellulose and the complex secondary and tertiary structures of the proteins make them impractical for producing electrospun nanofibers due to insufficient entanglement. Several methods, such as denaturation, the use of an appropriate solvent, and blending with other polymers, can be used to ensure the successful electrospinning of globular proteins ,19. In t3, viscosity: 400–620 cps at 25 °C, CAS #: 9005-65-6) was supplied from Merck .Carob flour was purchased from Havancızade Gıda Gıda Co., Inc. . Whey protein concentrate (80% protein on a dry-weight basis) was provided from Proteinocean Gıda Co. Inc. . Caffeic acid (CAS #: 331-39-5), polyethylene oxide , and sodium hydroxide pellets (CAS number: 1310-73-2) were obtained from Sigma Aldrich Chemical Co., . Polyoxyethylene sorbitan monooleate (Tween80) (density: 1.064 g/mw/v) homogenized solution. Then, carob flour (3% w/v) and whey protein concentrate  were added to the solution at different concentrations  for 24 h at 400 rpm to obtain 2.5%  at 10,000 rpm for 3.5 min. pH of the solutions was adjusted to 10 by adding 8 M NaOH. pH of the solutions was measured by pH Portable Meter . Temperature of the solutions was brought to 80 °C by microwave heating (450 W for 2.5 min) . At the same time, caffeic acid (CA) was dissolved in 80% aqueous ethanol by magnetic stirrer at 750 rpm for an hour. Then, it was incorporated into the electrospinning solutions to obtain 1% and 10%  of CA content in solid fibers. Tween80 (2% w/v) was added to the mixtures as surfactant. Then, final solution was stirred further by using magnetic stirrer for 30 min at 750 rpm for complete homogenization.Solutions were mixed with high-speed homogenizer  and the consistency index (k). Measurements were duplicated.Rheological properties of the solutions were analyzed by a controlled strain rheometer  with a titanium cone and plate geometry  at 25 °C for shear rates varying between 0.1 s® Cond 7110, Wissenschaftlich-Technische Werkstätten GmbH, Wheilheim, Germany) at room temperature. Measurements were conducted twice.Solution’s electrical conductivity was measured by using conductivity meter . The positively charged electrode was linked to the needle while the negatively charged electrode was linked to a metal collector wrapped with aluminum foil. The distance between the collector and the needle’s tip was fixed at 0.3 m. Experiments were carried out at room temperature with a relative humidity of 30–40%, a flow rate of 0.8 mL/h, and a voltage of 12 kV.Samples’ morphology was analyzed by using scanning electron microscopy (SEM) . Samples were coated with gold palladium and scanned at 10,000× magnification level. Diameters of nanofibers were measured from the SEM images by using Image J analysis software V 1.50i . Then, average fiber diameter was calculated from one hundred randomly selected nanofibers for each sample.The thermal analysis of the electrospun nanofibers was performed using a differential scanning calorimeter . Approximately 5 mg of sample was put into a hermetically sealed aluminum pan. As a reference, an empty pan was used. After cooling to –60 °C, each pan was heated to 250 °C at a rate of 10 °C/min. The glass transition temperature, melting temperature, and melting enthalpy of each sample were determined using differential scanning calorimetry (DSC) thermograms. The DSC measurements were carried out in duplicates .Thermogravimetric analysis (TGA) was conducted to examine the weight change of the fibers, caffeic acid, whey protein, carob flour, and PEO as a function of temperature by thermo-gravimetric analyzer (Perkin Elmer Pyris 1). About 5 mg of sample in powder form was heated from room temperature to 500 °C at a rate of 10 °C/min with nitrogen .The X-ray diffractometry (XRD) data of the nanofibers were obtained using Ultima IV X-ray diffractometer  with Cu Kα radiation in a range of 2θ = 5–70° .−1 in the wavenumber range of 4000–600 cm−1 [Nanofibers were analyzed using a Fourier transform infrared (FTIR) spectrometer  with an attenuated total reflectance (ATR) attachment. Data were collected at a resolution of 2 cm600 cm−1 .−1 ·s−1 ·Pa−1);WVP: water vapor permeability (g·mΔX: film thickness (m);−2·s−1);WVTR: water vapor transmission rate ;Pwo: partial pressure of the water vapor outside the cup (Pa).PModified version of ASTM E-96 method was used to determine water vapor permeability (WVP) [v/v) ethanol–water solution and left to dissolve for 2 h before centrifuging at 10,000 rpm for 3 min. An amount of 0.5 mL was added to 3.5 mL of 0.6 mM DPPH solution and incubated for 1 h in the dark. A spectrophotometer was used to measure the absorbance at 517 nm . Control was the mixture of 0.5 mL of 80% (v/v) ethanol–water solution and 3.5 mL of 0.6 mM DPPH solution. Methanol was used as blank. The measurements were taken twice. Using the formula below, the antioxidant activity (AA%) of the fibers was calculated.AA: antioxidant activity of fibers;control: absorbance of the control sample at 517 nm;Asample: absorbance of the fibers at 517 nm.AA modified version of the 2,2-diphenyl-1-picrylhydrazyl (DPPH) technique was used to determine the antioxidant activity of nanofibers containing caffeic acid . A totalThe method described in Aydogdu et al.  was usedThe biodegradability of films was assessed with some modification as reported by da Silva Filipini et al. . Weight p ≤ 0.05).Minitab software was used to conduct statistical analysis . ANOVA was used to determine whether there were any significant differences between treatments. Tukey’s multiple comparison test was used to determine whether there were significant differences in the data .p = 0.05). Average fiber diameters of the films ranged between 222 nm and 310 nm. The sample of 3C3W presented the highest average fiber diameter (310 ± 70 nm). Higher k values in solutions resulted in nanofibers with higher diameters as the number of molecular entanglements in the solution rose [Shear-thinning behavior was observed since the apparent viscosity of the solutions decreased as the shear rate increased. Flow behavior index values (n), which were less than 1, also validated that the solutions had shear-thinning properties. Shear-thinning behavior has also been observed in other electrospinning studies ,27,33,34ion rose . In addiion rose . Figure The addition of WPC reduced the sticky appearance of the nanofibers. In addition, as shown in p < 0.05). After the addition of caffeic acid to the solution, the pH of the solution was adjusted again to alkaline conditions with the addition of NaOH. Alkaline solutions contain many Na+ and OH− ions, thus causing an increase in the conductivity [As can be seen in uctivity . When couctivity , the nanuctivity . The samuctivity .g), the melting temperature (Tm), and enthalpy change (ΔHm) of the nanofibers.g) is related to the softening point from the glassy state to rubbery state. Tg values of nanofibers decreased significantly with an increase in WPC concentration. Additionally, in the study of Ignatova et al. [g decreased. Having both Tm and Tg values indicates that nanofibers had a semi-crystalline structure showing both amorphous and crystalline properties. Tm indicates the melting point of the crystalline phase. The melting temperature of the nanofibers varied between 60.9 and 63.9 °C, which was lower than melting temperature of pure PEO. The Tm of PEO was reported as 71.5 °C [m was higher when the whey protein concentration increased, supporting this reason. The other reason might be related to the working principle of electrospinning. During electrospinning, the rapid solidification process of the stretched chains causes the polymer chains to remain in a mostly noncrystalline state [The glass transition temperature  was used to detect the interactions and linkages in the structure of nanofibers. FTIR spectra of the nanofibers are shown in −1 and 2850 cm−1. Peaks at roughly 2850 cm−1 belong to the methylene group molecular stretching, whereas peaks at 1100 cm−1 correspond to the combination of the ether and stretching methylene groups in PEO, as indicated in the previous studies [−1 region and also the increase in the peak intensity parallel to the WPC concentration indicated the presence of WPC. The nanofibers’ spectra revealed a distinctive peak at about 1650 cm−1, which was linked to the amide-I region and found in proteins. The amide-II band, which occurs predominantly due to N=H in-plane bending vibrations, is ascribed to the band that appears at around 1540 cm−1. This peak was also visible in the spectrum of pure whey protein powder [−1 belonged to the section of starch, amylose, and amylopectin monomer glucose units which was found in the carob flour. Peaks near 960 cm−1 in the spectra of nanofibers were attributed to vibrations caused by C-O-C glycosidic linkages. The resulting composite fibers retained all the characteristic bands associated with PEO, carob flour, and WPC. However, minor differences in the spectra of caffeic-acid-encapsulated nanofiber were discovered. Similar to another study revealing the spectrum of caffeic acid, peaks at 1650 cm−1 due to the carboxyl group (C=O), and peaks at 1614 cm−1, 1608 cm−1, and 1456 cm−1 coming from the aromatic ring (C=C), were observed [−1 (3C3W) to 1276 cm−1 (3C3W10CA), and also changes in the peak intensities, for example, methylene stretching at 2850 cm−1, were less intense for caffeic-acid-enriched films than the control film, and could be attributed to an interaction between caffeic acid and the polymers in the nanofiber’s composition. The shift in the amide-III was also observed in the caffeic-acid-containing starch–chitosan film. It was suggested that this might be due to the electrostatic interaction between the charged amino group (NH+3) of chitosan (or gelatin) and the charged carboxylic group (COO−) of antioxidants’ phenolic ring (ferulic acid and caffeic acid) [The presence of the PEO was confirmed by the peaks at about 1100 cm studies ,41. Peakn powder ,35. As wobserved . The shiic acid) . These f−10 g·m−1 ·s−1 ·Pa−1.One of the most essential features in food packaging applications is water vapor permeability (WVP). Food packaging materials with adequate barrier qualities can improve packaging conditions by reducing moisture transfer between the food product and the environment . The WVP−12 g·m−1·s−1 ·Pa−1 [The comparison of WVP values between different samples revealed the effect of whey protein concentration variation in nanofibers and antioxidant incorporation into nanofibers. In another study, the WVP of a carob–rice starch–PEO blend of electrospun fibers ranged between 2.73 and 1.68 × 10−1 ·Pa−1 . The WVP−1 ·Pa−1 . Nanofib−1 ·Pa−1 . In anot−1 ·Pa−1 . Althoug−1 ·Pa−1 , caffeic−1 ·Pa−1 .The efficiency of loading the caffeic acid into the nanofibers and the antioxidant activity of the nanofibers are given in The loading efficiencies were 76.4% and 94.0% for 1% and 10% CA concentrations, respectively. It can be concluded that electrospinning is a very efficient process for encapsulating sensitive bioactive compounds since it is performed at room temperature. Nanofibers containing 10% caffeic acid had an extremely high encapsulation efficiency. It was higher than those previously reported for other encapsulation technologies. In the study by Fathi et al. , the encBiodegradation is the biochemical material conversion process in water, biomass, carbon dioxide, or methane. The biodegradation of the polymer is divided into two stages. First, the process of reducing polymer chain carbon bond breakage in terms of heat, humidity, and the presence of microorganisms occurs. Second, when shorter chains become energy sources for microorganisms which are bacteria, fungi, or algae, part of the biodegradation process begins. Complete biodegradation is achieved when carbon compounds are converted into water, biomass, or carbon dioxide by micro-organisms . BiodegrThe reduction in the area of the biodegraded films was observed every five days until the degradation was complete. The first sign of deterioration in the films was a shift in color. At the end of the fifth day, the color of the films had darkened, and the films had shrunk. In the following days, the films’ original appearance and structural integrity had been deteriorated, revealing rougher, degraded surfaces with porous pits and holes. After 15 days, the films were significantly disintegrated. At the end of the 20th day, the areas of the samples were significantly reduced. da Silva Filipini et al.  reported−10 to 1.38 × 10−10 g·s−1 ·m−1 ·Pa−1. CA inclusion in carob–WPC–PEO electrospun nanofibers was validated morphologically, chemically, and thermally. When the concentration of CA was increased, the diameter of the fibers decreased. CA nanofibers synthesized in this study exhibited a uniform and bead-free structure. The FTIR spectrum confirmed that there was a molecular interaction between CA and carob–WPC–PEO. Nanofibers containing 10% CA with 94% loading efficiency have shown that electrospinning is an effective approach to encapsulating bioactive chemicals into biomaterials. They also had 92.95% antioxidant activity, proving that CA is a suitable material for active packaging because of its antioxidant characteristics. TGA and DSC results also support the loading efficiency findings. Finally, CA-loaded environmentally friendly nanofibers developed in this study may be a promising material for novel active packaging applications requiring a high antioxidant activity and biodegradation rate. Due to their weak mechanical capabilities, it is advised that these nanofiber films with antioxidant properties be used in combination with another packaging material to make multilayered packaging.Caffeic acid was successfully encapsulated in carob flour and whey-protein-based electrospun nanofibers in this study. The increase in the WPC concentration led to an increase in the fiber diameter; thus, water vapor barrier properties were improved from 2.95 × 10


Antimicrobial resistance (AMR) is a critical issue in health care in terms of mortality, quality of services, and financial damage. In the battle against AMR, it is crucial to recognize the impacts of all four domains, namely, mankind, livestock, agriculture, and the ecosystem. Many sociocultural and financial practices that are widespread in the world have made resistance management extremely complicated. Several pathways, including hospital effluent, agricultural waste, and wastewater treatment facilities, have been identified as potential routes for the spread of resistant bacteria and their resistance genes in soil and surrounding ecosystems. The overuse of uncontrolled antibiotics and improper treatment and recycled wastewater are among the contributors to AMR. Health-care organizations have begun to address AMR, although they are currently in the early stages. In this review, we provide a brief overview of AMR development processes, the worldwide burden and drivers of AMR, current knowledge gaps, monitoring methodologies, and global mitigation measures in the development and spread of AMR in the environment. We continue to face an antimicrobial-resistance challenge from drug-resistant microorganisms that are continuously developing novel resistance mechanisms. This puts our ability to cure common ailments in jeopardy. Numerous bacteria are acquiring resistance to a wide variety of drugs, and these germs cause infections that are not treatable with the currently available antimicrobials. This is perhaps the most troubling part of the issue . AlthougResistance is driven by a variety of factors that are complex, diverse, and cross-sectoral in nature. In addition to sharing the same habitat, humans and animals also share several infectious diseases that may have started in animals at some point during their evolution into humans ,5,6. TheSignificant changes in food intake as a consequence of increased population density may have been one of the driving forces behind the resistance. This is in addition to the global sanitation and water pollution concerns posed by sewage, manure runoff, and waste generated by pharmaceutical manufacturing and hospitals. AMR is expected to inflict an estimated 10 million losses by 2050 , and somIdentifying and regulating the importance of environmental factors in the genesis and progression of AMR is crucial to effectively combat AMR globally. Identifying and addressing the variables that contribute to AMR in the population will be crucial to develop and implement effective prevention and management strategies for AMR, as well as to monitor the environmental sources of AMR. This study highlights the causes of antibiotic resistance, as well as existing knowledge gaps, surveillance approaches, and international mitigation efforts.In a recent estimate, about 3.57 million of 4.95 million deaths worldwide were associated with antimicrobial resistance in 2019 . There hKlebsiella pneumoniae in neonatal sepsis [In the past, the continuous discovery of new antibiotic classes helped reduce the burden of infectious diseases; however, pathogenic bacteria were able to develop resistance to these new compounds within a few years . For exal sepsis . Furtherl sepsis , and theThe risks of AMR are projected to increase dramatically as the routine antibiotic treatment regimen becomes obsolete, possibly leading to scenarios in which terminally ill patients need palliative care, but the medications provided are no longer clinically effective  and hospFrom the early utilization of prontosil and the discovery of penicillin by Alexander Fleming in 1928 through the close of the previous century, remarkable breakthroughs have been achieved in the study and manufacture of anti-infective drugs, contributing to the major advancement of modern medicine . Howevermcr-1 gene conferring resistance to the nephrotoxic last-line antibiotic drug colistin was found in Escherichia coli isolates from unprocessed meat, livestock, and infected people [mcr-1 gene was identified in more than twenty different nations [Staphylococcus aureus associated with livestock methicillin (LA-MRSA) was first described in 2005 [Staphylococcus spp. (VRSA) have acquired vanA (the gene conferring resistance to vancomycin) from animal-associated VRE [The first incidents of tolerance to antibiotics in food-producing animals were observed in 1951, following the administration of streptomycin to turkeys . Recentld people . Within  nations ,31. Furt nations . Staphyl in 2005 . As a co in 2005 ,35. Conc in 2005 . More im in 2005 ,38. Thro in 2005 . Enterocated VRE . Enterocated VRE ,42.Salmonella typhimurium strain harboring blaNDM-5 has been discovered in commercial pork for the first time in Jiangsu province in China [Salmonella enteritidis resistant to nalidixic acid and showed that the source of contamination was chicken sandwiches [Salmonella enterica serovar Heidelberg in Québec and Ontario was significantly related to chicken intake and inappropriate antibiotic use in poultry animals. Furthermore, Campylobacter jejuni cultured from commercial chicken items has been shown to be substantially related to clinical human samples from the United States [A in China . Diaz etndwiches . A studyndwiches . Otto etndwiches  observedd States . These eEnterobacter cloacae develop high-level resistance to carbapenems as a result of a porin mutation that reduces carbapenem absorption and increases the synthesis of a chromosomal β-lactamases [The exposure of bacteria to subinhibitory concentrations of antibiotic drugs is one of the primary causes of antimicrobial resistance (AMR), which is mainly caused by the improper use of antibiotics in clinical and agricultural settings ,49. Antictamases .The development of antibiotic resistance in bacteria often occurs through vertical and horizontal gene transfer. A mechanism known as “vertical gene transfer” passes genetic information, including any mutations, from one generation to the next within a family. Horizontal gene transfer (HGT) is the primary mechanism through which antibiotic-resistance genes are rapidly disseminated across several bacterial species . ResistaThe overuse and misuse of existing antimicrobials have accelerated the emergence of AMR at an alarming rate . BetweenE. coli on vegetable salads, fresh milk, raw chicken and beef, and on the surface of raw eggs. Four percent of these samples had extended-spectrum β-lactamase activity.Since the 1950s, there has been a steady increase in the demand for meat and dairy products around the world, leading to an increase in the use of antimicrobial drugs in agriculture , since tBiocides are commonly used antimicrobial agents in health-care facilities, beauty brands, residential disinfecting products, wipes and home furnishing additives, farmyards for applications, including wheel and foot rinses, and a wide range of factory methods, such as with the grasp of fouling and souring of piping systems along with oil wells  . There aIn contrast to antibiotics, biocides generally have several target sites . The effThe general mechanism of action of the biocide may be described by the structure of the bacteria against which it is most effective. There are three layers of interaction: cellular components outside the cell, with the cytoplasmic membrane, and with cytoplasmic components. To develop its antimicrobial effect, a biocide can interact with bacterial cells at one or all three levels . The bacPseudomonas resistance to certain concentrations of alcohols [The resistance processes to biocides depend on the structural properties of the bacteria, which may be inherent or acquired. Intrinsic resistance is an inherent chromosome-regulated characteristic of a bacterial cell that allows it to evade a biocide. Bacteria that produce endospores are significantly more resistant to biocides such as clostridium and bacillus. Furthermore, physiological adaptation is believed to contribute significantly to increased bacterial resistance to biocides, such as in the case of alcohols .E. coli, Proteus mirabilis, Pseudomonas aeruginosa, and S. marcescens are examples of bacteria resistant to chlorhexidine, a common hospital disinfectant [Pseudomonas and the qacA/B genes found in S. aureus, which confer resistance to QACs [Similar to antibiotic resistance, the development of biocide resistance is mutational or acquired ,88,89. Enfectant ,91. Plasnfectant . Many ba to QACs . ResistaA small number of biocides have been shown to be inactivated, and this applies only to a subset of the total number of biocides. As a result of the abundance of cellular targets that biocides can attack, protection is most likely and often derived from variations in the permeability of the cell membrane or enhanced biocide outflow . The BioResidential effluent, sanitary sewers, industrial effluent and emissions, and traffic-related output are the main metropolitan sources of heavy-metal ions . It has Antibiotic- and metal-resistance mechanisms have several structural and functional similarities, such as drug and metal sequestration, reduced membrane permeability, drug and metal efflux, drug and metal alteration, and alteration of cellular targets . There iHeavy metals in the influent can have harmful consequences, which can affect biological wastewater treatment processes ,105, sucIt is not clear why certain microorganisms are more effective than others in removing metals from the environment; however, this might be due to differences in genetics or in the reduction medium . BecauseIt is estimated that a significant amount of the medication held by users is expelled in the activated form in fecal matter . Human-eAnimal fecal matter has been found to contaminate the ecosystem with drug-tolerant bacteria and antibiotics . WheneveThe antibiotics discovered in the sludge are mainly less permeable to water, such as norfloxacin, ofloxacin, ciprofloxacin, trimethoprim, sulfamethoxazole, and doxycycline . The amoVarious methods have been extensively utilized for the monitoring of AMR in the environment. Some of these methods, i.e., culture-based, molecular-based, and nanotechnology-based, are discussed in the following.The microbe culture medium (solid/semisolid/broth) in which microbes are grown and quantified in research experiments has traditionally been considered the gold-standard methodology for the detection of antimicrobial-resistant bacteria. Culture-based techniques are inexpensive, relatively precise, and convenient. Antimicrobial-resistant microorganisms can be effectively obtained from samples by incorporating antimicrobials of interest in the growth medium for selection, and if analogous experiments are performed lacking antimicrobials, the fraction of a microbial species that appears to be tolerant can be assessed. For certain antibiotics, such as colistin, the broth microdilution minimum inhibitory concentration (MIC) test is still considered the gold standard for determining sensitivity.Although this method of AMR surveillance is widely adopted, it has enormous constraints. Many pathogens encountered in the habitat cannot be cultivated under laboratory conditions, false-negative results can arise from samples contaminated with high concentrations of chemicals, and the cultivation phase can be time-consuming, demanding prolonged incubation, numerous steps, and validation assays. The methodology adopted to preserve the specimens, as well as the preservation period, could have a significant impact on the rescue and enumeration of specific entities. Undoubtedly, the major drawback of these techniques is the relatively limited productivity.® system [™  [Various culture-based automation technologies have been introduced to accelerate lab cultivation and assessment. For example, the VITEK® system  (BioMéri® system  (BeckmanCompany) , and TheCompany)  , insertion sequences (IS), or plasmid-associated genes, which were typically coupled to HGT, are notable examples of targets.The nucleic acid amplification test (NAAT) is based on the polymerase chain reaction (PCR) that can assess the existence or omission of a targeted gene. In any given sample, ARGs of interest can be detected using specific DNA probes in a conventional PCR experiment. Quantitative PCR (qPCR) provides real-time results and precise statistical outcomes with response rates that are faster than conventional PCR. Using qPCR is a useful technology to investigate the effectiveness of actions on AMR . EpicPCR® [® (ROCH), both of which depend on multiplex qPCR technology [Molecular techniques based on PCR are rapid compared to culture-based approaches, and thus can uncover various ARGs, even in bacteria that were challenging to cultivate in laboratories . However®  (Cephid)chnology .® HiFi technology [The most precise and accurate molecular tool for studying a specific organism and its ARGs to date is the utilization of whole-genome sequencing (WGS). Here, the whole DNA of an organism can be screened, along with ARGs, their copy numbers, mutations, and novel resistance genes. The most used platforms for WGS worldwide are Illumina  (Illuminchnology  (Pacific® (Bruker) [® MS [Recently, protein profiling of organisms using mass spectroscopy techniques has been implemented for bacterial identification. Specifically, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-ToF MS) is a recent molecular technique that evaluates AMR in microbial specimens. Identifying bacteria with MALDI-ToF is rapid, very accurate, and can process larger volumes than regular molecular techniques. Unfortunately, it requires an isolated bacterium and access to a specialized database, and the initial setup investment is costly. However, many advances have been made in the detection of ARGs using MALDI-ToF instruments, and the machine has been adopted by many hospitals around the world. The two major brands of MALDI-ToF technology in microbial identification are MALDI Biotyper(Bruker)  and VITEr) [® MS   is implementing a collaborative strategy in the European Council with the objective of integrating regional research initiatives to better combat the threat of AMR. Network mapping describes countless resources and propagation mechanisms, all of which are presumably driven by the ever-increasing incidence of AMR in therapeutic, animal, and ecological contexts. In addition, the Combating Antibiotic Resistant Bacteria Biopharmaceutical Accelerator (CARB-X) offers financial support to companies that are developing novel and potentially effective solutions to antibiotic resistance . HoweverIn April 2014, the World Health Organization recognized AMR as a “significant universal challenge.” The World Health Assembly, the governing body of all WHO member nations, then announced the Global Action Plan on Antimicrobial Resistance  , encouraA major drawback in the fight against AMR is rapid diagnostic testing, an additional pressing issue, especially in underdeveloped nations where conventional microbiological technologies have been routinely used to identify pathogens. These inadequacies could be resolved by creating custom therapeutics for appropriate antimicrobial treatment based on modern and advanced genomic screening technologies. A One-Health strategy would perhaps represent a significant approach for exploring the human–animal interplay and introducing innovative assessment tactics. Taking into account the routes of transmission of antimicrobial resistance that have been known to be prevalent among the three elements, including people, animals, and the environment , these aA limitation of innovative drugs mainly hampers the analysis of the whole issue of AMR ; therefoARGs possess the ability to transition from bacterial populations between human and livestock infections, and conversely, since gene exchange among bacteria might happen anywhere theoretically, it is particularly prone to occur among taxonomically highly associated bacteria. Furthermore, in reality, to transfer resistance, host and recipient bacteria must occupy an equivalent biological habitat, at least for a short period. Taking this into account, it makes perfect sense that the prevalence of resistance transmission that occurs between animal- and human-associated microbes should be stronger. The true consequence of either of such environmental circumstances appears to be unclear because of the limited knowledge of the mechanisms that cause the transference of ARG in bacterial populations and the subsequent persistence functionality of ARG or host bacteria when transported under such circumstances.In addition to the increasing international recognition of antimicrobial resistance, we still have significant gaps in our awareness in terms of the prevalence, location, and drivers of AMR at the community level. To increase our knowledge of the severity of AMR in communities, as well as the population-level variables that impact the establishment and distribution of AMR, we must incorporate a wide range of context-specific epidemiological research methodologies. A comprehensive and cost-effective AMR approach must incorporate a multidisciplinary framework to reduce antibiotic consumption, improve monitoring and control, and strengthen the user and general practitioner in the administration of antibiotics. Although the existing severity of the situation and its distribution in both the population and hospitals complicate the situation, it is essential, as health-care personnel contribute greatly to minimizing the genesis and transmission of resistance. This research, in collaboration with management programs, will help find approaches to the recognition, mitigation, and monitoring of AMR, as well as the prolonged use of antimicrobials in the treatment regime.


Heme biosynthesis represents a crucial pathway due to its paramount importance in oxygen transport, energy production and drug metabolism. Recently, we described a “heme biosynthesis mRNA expression signature” that correlates with histopathological glioma grade and survival. The aim of the current study was to correlate this heme biosynthesis mRNA expression signature with diagnostic molecular markers and investigate its continued prognostic relevance.The prognosis of diffusely infiltrating glioma patients is dismal but varies greatly between individuals. While characterization of gliomas primarily relied on histopathological features, molecular markers increasingly gained importance and play a key role in the recently published 5In this study, patient data were derived from the “The Cancer Genome Atlas” (TCGA) lower-grade glioma and glioblastoma cohorts. We identified diffusely infiltrating gliomas correlating molecular tumor diagnosis according to the most recent WHO classification with heme biosynthesis mRNA expression. The following molecular markers were analyzed: EGFR amplification, TERT promoter mutation, CDKN2A/B homozygous loss, chromosome 7 + /10- aneuploidy, MGMT methylation, IDH mutation, ATRX loss, p53 mutation and 1p19q codeletion. Subsequently, we calculated the heme biosynthesis mRNA expression signature for correlation with distinct molecular glioma markers/molecular subgroups and performed survival analyses.p < 0.0005), without 1p19q codeletion (p < 0.0005), with homozygous CDKN2A/B loss (p < 0.0005) and with EGFR amplification (p = 0.001). Furthermore, we observed that the heme biosynthesis mRNA expression signature increased with molecular subgroup aggressiveness (p < 0.0005), being lowest in WHO grade 2 oligodendrogliomas and highest in WHO grade 4 glioblastomas. Finally, the heme biosynthesis mRNA expression signature was a statistically significant survival predictor after multivariate correction for all molecular markers (p < 0.0005).A total of 649 patients with available data on up-to-date molecular markers and heme biosynthesis mRNA expression were included. According to analysis of individual molecular markers, we found a significantly higher heme biosynthesis mRNA expression signature in gliomas with IDH wildtype (Our data demonstrate a significant correlation between heme biosynthesis regulation and diagnostic molecular markers and a prognostic relevance independent of these established markers. Consequently, heme biosynthesis expression is a promising biomarker for glioma aggressiveness and might constitute a potential target for novel therapeutic approaches. Diffusely infiltrating gliomas are the most frequent brain tumor entity in adult patients suffering from primary central nervous system (CNS) tumors . WheneveRecently, we investigated the role of the heme biosynthesis pathway in the biology of diffusely infiltrating gliomas , 2021. Bth edition of the WHO classification of CNS tumors, additional molecular markers for glioma diagnostics were introduced  glioma dataset  with relrization . In the th edition of the WHO classification on the recently described heme biosynthesis mRNA expression signature  provided by the National Cancer Institute and Human Genome Research Institute as well as classification of molecular profiling data generated by the TCGA Research Network within a study on molecular profiling in diffuse gliomas published in 2016 . Gene exst 2021 . Further for intraoperative tumor visualization to the actively fluorescing metabolite protoporphyrin IX (PpIX), the heme biosynthesis mRNA expression signature was applied as described earlier . Factorsth edition of the WHO glioma classification  as well as after correction for the complete set of all nine investigated molecular markers .t-tests. To control for multiple testing, markers with significant results in this univariate analysis where then included in a multivariate linear regression model, and the p-values derived from this model were finally used to detect differences according to distinct markers. In the second step, distinct molecular subgroups according to the WHO classification were defined and mRNA expression signature was investigated between these subgroups using ANOVA with post hoc Fisher’s least significant difference (LSD) test between distinct subgroups. For the survival analysis, Cox regression models including the metric mRNA expression signature variable alone or in in combination with either one distinct molecular marker or the complete set of all nine molecular markers were created. Molecular markers were used as categorical variables . While inferential statistical analysis was performed using the metric heme biosynthesis mRNA expression signature variable, patients were categorized into three equally sized mRNA expression signature subgroups  for survival visualization in Kaplan-Meier curves. Multiple testing was addressed by using a multivariate regression model and ANOVA with post hoc Fisher’s LSD ensuring that type I error for each main hypothesis remained at 5% and thus the resulting p-values were considered statistically significant under the commonly used threshold of p = 0.05.Statistical investigations were performed using SPSS statistical software . Descriptive analyses included patient age, gender, histopathological tumor subtypes and WHO grades as well as the mRNA expression signature and the molecular marker status for the entire cohort and subgroups according to IDH status. In the initial step, where the mRNA expression signature was visualized according to separate molecular markers, presence of a difference between the groups was tested using unpaired From altogether 1122 patients with available data on the molecular marker status, sequencing data were available for 649 patients that therefore formed our study cohort. An IDH mutation was present in a majority of 423 (65.2%) of patients as compared to 226 (34.8%) patients with IDH wildtype tumors. The median patient age of the entire cohort was 47 years (14–89 years) with a male: female ratio of 1.36:1. Further details on the patient characteristics of the entire study cohort as well as IDH mutant and IDH wildtype subgroups are provided in With regard to the investigated molecular markers, IDH mutations were found in 423 (65.2%) patients and 1p19q codeletion was detected in 169 (26.0%) patients. Further, ATRX loss was present in 194 (29.9%) of 648 cases, whereas no ATRX status was available in 1 case. Moreover, Tp53 mutations were observed in 294 (45.3%) patients. Furthermore, 7 + /10- aneuploidy was present in 149 (23.0%) patients and EGFR amplification was observed in 186 (28.7%) cases. Finally, MGMT promoter methylation was detected in 467 (75.2%) of 621 patients, whereas the methylation status was missing in the remaining 28 cases. An overview of the frequency of molecular marker expression in the entire cohort as well as subgroups according to IDH mutation status is provided in p < 0.0005), without 1p19q codeletion , ATRX wildtype , 7 + /10- aneuploidy , EGFR receptor amplification , homozygous CDKN2A/B loss  and unmethylated MGMT promoter status . In contrast, no significant differences in the mRNA expression signature were observed according to Tp53 mutational status  and TERT mutational status .In the initial univariate analysis, significantly higher mRNA expression signature were observed in tumors with IDH mutations , 1p19q codeletion (p < 0.0005), homozygous CDKN2A/B loss (p < 0.0005) and EGFR amplification (p = 0.001) as independent factors influencing heme biosynthesis mRNA expression. In contrast, no independent effect was present for 7 + /10- aneuploidy (p = 0.069), ATRX status (p = 0.176) and MGMT promoter methylation (p = 0.733). The relation of mRNA expression signature according to individual molecular markers with p-values representing the results of the multivariate analysis are shown in The subsequent multivariate analysis was calculated for molecular markers with a significant impact on mRNA expression signature in the univariate analyses. According to these data, the multivariate regression model identified the IDH mutational status  tumors could be unambiguously assigned to a distinct molecular subgroup. Of these, 164 (26.5%) tumors were classified as IDH mutant and 1p/19q codeleted oligodendrogliomas, 252 (40.7%) tumors were classified as IDH mutant astrocytomas and the remaining 203 (32.8) tumors as IDH wildtype glioblastomas. Regarding the WHO grade, grade 2 accounted for 93 (56.7%) cases of IDH mutant and 1p/19q codeleted oligodendrogliomas and 122 (48.4%) cases of IDH mutant astrocytomas. In contrast, WHO grade 3 was present in 71 (43.4%) cases of IDH mutant and 1p/19q codeleted oligodendrogliomas and 101 (40.1%) cases of IDH mutant astrocytomas. Furthermore, WHO grade 4 was present in 29 (11.5%) cases of IDH mutant astrocytomas. In contrast, a total of 30 (4.6%) cases could not be clearly categorized due to missing information on histological WHO grade in 5 cases (0.8%) and molecular marker combinations not consistent with any specified glioma subgroup in 25 cases (3.8%) and were thus not included in the molecular subgroup analysis. It is of note, that 139 (59.9%) cases of glioblastomas and IDH mutant WHO grade 4 astrocytomas were histopathologically classified as WHO grade 4 tumors, whereas the remaining cases were histopathologically classified as grade 2  and grade 3  tumors and subsequently classified as WHO grade 4 solely due to molecular markers.p < 0.0005), and the post hoc subgroup analysis demonstrated significant differences between all distinct subgroups except for oligodendrogliomas WHO grade 3 and astrocytomas WHO grade 2. The distribution of mRNA expression signature according to molecular glioma subgroups is visualized in post hoc analysis comparing pairs of distinct subgroups is provided in The mRNA expression signature values increased with the aggressiveness of the molecular subgroups: (1) -28.28 ± 1.92 in IDH mutant and 1p/19q codeleted oligodendrogliomas WHO grade 2, (2) -26.90 + 2.43 in IDH mutant and 1p/19q codeleted oligodendrogliomas WHO grade 3, (3) -26.16 ± 2.27 in IDH mutant astrocytomas WHO grade 2, (4) -25.24 ± 2.80 in IDH mutant astrocytomas WHO grade 3, (5) -23.22 ± 3.78 in IDH mutant astrocytomas WHO grade 4 and (6) -19.55 ± 3.55 in IDH wild type glioblastomas WHO grade 4. The overall differences between these subgroups were statistically highly significant . The resulting interaction graph is shown in B = 0.095, p < 0.0005), ATRX loss , 1p/19q codeletion , TERT , 7 + /10- aneuploidy , EGFR amplification , homozygous CDKN2A/B loss , TP53 mutation  and MGMT promoter methylation . Kaplan-Meier survival curves stratified for mRNA expression signature subgroups after correction for distinct molecular markers are shown in B = 0.093, p < 0.0005) after multivariate correction for all molecular markers in a single cox-regression model. Overall survival curves after correction for all nine molecular markers are shown in Overall, we found a median overall survival of 1547 days in the entire cohort. With regard to the IDH mutational status, the median overall survival was markedly longer in IDH mutant gliomas  as compared to IDH wildtype gliomas . In a subsequent analysis, we tested if the heme biosynthesis mRNA signature has an additional survival impact beyond the one associated with molecular glioma markers. For this analysis, we performed Cox-regression analyses and plotted Kaplan Meier curves after correction for the molecular marker status. After initial correction for one distinct molecular marker at a time, a highly significant survival impact of the heme biosynthesis signature could be demonstrated after correction for each investigated marker including IDH mutation . Written informed consent from the [patients/participants OR patients/participants legal guardian/next of kin] was not required to participate in this study in accordance with the national legislation and the institutional requirements.GW, FE, and MrM performed conception and design of the study, BK, TR-P, MB, AL, MtM, LW, SH-J, JP, and MSB contributed to the conception and design of the study. MrM managed the database. MrM, FE, and MB performed the statistical analysis. FE and MrM wrote the first draft of the manuscript. GW and JP finalized the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.


After spinning, the properties of the nanofibrous scaffolds were characterized. In vitro drug discharge behavior was also evaluated. Furthermore, the effectiveness of the DCB–PLGA-composite nanofibers in repairing ruptured Achilles tendons was investigated in an animal tendon model with histological analyses. The experimental results show that, compared to the pristine PLGA nanofibers, the biomolecule-loaded nanofibers exhibited smaller fiber size distribution and an enhanced hydrophilicity. The DCB-composite nanofibers provided a sustained release of doxycycline and bupivacaine for over 28 days in vivo. Additionally, Achilles tendons repaired using DCB-composite nanofibers exhibited a significantly higher maximum load-to-failure than normal tendons, suggesting that the biomolecule-incorporated nanofibers are promising scaffolds for repairing Achilles tendons.Achilles tendon rupture is a severe injury, and its optimal therapy remains controversial. Tissue engineering scaffolds play a significant role in tendon healing and tissue regeneration. In this study, we developed tri-layered doxycycline/collagen/bupivacaine (DCB)-composite nanofibrous scaffolds to repair injured Achilles tendons. Doxycycline, collagen, and bupivacaine were integrated into poly(lactic- Tendons are soft tissues that link muscles to bones, enabling passive and active joint movement during force transmission. They have a complex hierarchical structure of collagen (mainly type I) and elastin embedded in a proteoglycan matrix ,2. CollaDespite progress in modern medicine, the treatment of ruptured tendons remains a challenge. Various methods, such as ultrasound therapy, platelet-rich plasma injection, local or systemic drug administration, and the supplementation of growth factors targeted at the injured site, have been proposed to treat Achilles tendon rupture ,15,16,17Doxycycline is an antibiotic medicine that shows experimental and clinical efficacy in treating tendon rupture and a feasibility for local applications during surgery or systemic administration ,22,23. Cco-glycolic acid) (PLGA), the copolymer of PGA and PLA, has been intensively studied because it can induce favorable tissue integration, exhibit a degradation rate similar to that of native collagen matrices, and deliver loaded drugs to the target site [Biodegradable polymers, such as polyglycolide (PGA), polylactide (PLA), and polycaprolactone (PCL), are used in vitro and in vivo in various medical fields ,19,21,25get site ,27,28.In this study, we developed doxycycline/collagen/bupivacaine (DCB) PLGA nanofibrous scaffolds to repair injured Achilles tendons. An electrospinning technique was employed to integrate doxycycline, collage, and bupivacaine layer by layer into PLGA nanofibrous membranes as healing promoters, 3D scaffolds, and painkillers, respectively. The properties of the prepared nanofibrous mats were characterized after spinning. The in vivo and in vitro drug release behavior was evaluated. In addition, the efficacy of the electrospun DCB-composite nanofibers in repairing ruptured Achilles tendons was investigated using a rat tendon model. Finally, a histological analysis was performed.The polymeric material was a commercially available PLGA polymer . The biomolecules, including doxycycline, collagen type I, and bupivacaine, were purchased from Sigma-Aldrich . The solvent, 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP), was acquired from Sigma-Aldrich .Tri-layered doxycycline-, collagen- and bupivacaine-loaded nanofibrous scaffolds were prepared by electrospinning technique. To fabricate the doxycycline-loaded nanofibrous layer, doxycycline (280 mg) and PLGA  were mixed with HFIP (5 mL). The mixture was subsequently electrospun into nanofibers by a lab-scale setup involving a syringe/needle , a high-voltage supply , and a grounded collector. For the preparation of collagen/PLGA and bupivacaine/PLGA nanofibrous layers, collagen (0.2 mL), PLGA , bupivacaine (280 mg), and PLGA  were separately dissolved in HFIP (5 mL) and electrospun into nanofibers. The distance between the collector plate and needle tip was 15 cm, the voltage used was 17 kV, and the delivery speed of the solutions was 0.7 mL/h. All procedures were performed at room temperature (27 °C) and 70% humidity. Tri-layered DCB nanofibrous mats with a thickness of approximately 200 µm were then acquired. The fabricated biomolecule–PLGA nanofibers were subsequently incubated in a vacuum oven at 40 °C for 3 days to vaporize the HFIP.The morphological structure of the electrospun biomolecule-loaded PLGA nanofibrous scaffolds was analyzed using a JSM–7500F scanning electron microscope  after coating with gold. The size distribution of the target nanofibers was defined using an image analysis program .membraneρ and polymerρ represent the densities of the biomolecule–PLGA and pristine PLGA nanofibers, respectively.In addition, the density of the nanofiber was determined by dividing the mass by volume. The porosity of the nanofibrous scaffold was calculated as follows:To determine hydrophilicity, the wetting angles of the nanofibers were assessed using a water contact angle analyzer . Electrospun PLGA nanofibers (10 × 10 mm) were placed on the testing plate, and distilled water was carefully dropped on their surfaces. The wetting angles were then analyzed using a camera.The thermal behavior of pristine PLGA and biomolecule–PLGA nanofibrous scaffolds was measured using differential scanning calorimetry (DSC) . The samples were scanned from 30 °C to 300 °C and the heating rate was maintained at 10 °C/min.The tensile strength of the electrospun nanofibrous scaffold was evaluated using a Lloyd tester  equipped with a 2.5 kN load cell. Specimens (2 × 5 cm) were cut from the spun membranes for the experiments. The extension rate was maintained at 60 mm/min, and the stress–strain curves were monitored.An elution scheme was utilized to quantify the in vitro release profiles of doxycycline and bupivacaine from the PLGA nanofibers. Nanofiber samples were immersed in a medium composed of phosphate buffer  prior to quantification. First, a nanofibrous mat with a controlled size  was sliced and incubated in a test tube containing 1 mL of phosphate-buffered saline at a constant temperature (37 °C) for 24 h. The phosphate buffer (1 mL) was replaced every 24 h until the mat was completely solvated. The concentration of drugs in the buffer was assessed using a Hitachi L-2200 high-performance liquid chromatography (HPLC) system .Sprague Dawley (SD) rats, each weighing approximately 250 ± 25 g, were used for in vivo experiments. All animal studies were approved by the Institutional Animal Care and Use Committee of Chang Gung University , and all animals were handled in accordance with the guidelines and regulations of the Ministry of Health and Welfare of Taiwan. The animals were sedated under general anesthesia in a polymethylmethacrylate chamber (40 cm× 20 cm× 28 cm) using isoflurane. Inhalational anesthesia was maintained throughout the operational process. For analgesia and hemostasis, the rats were injected with 0.5 mL 2% xylestesin-A and epinephrine mixture  in the right leg before the surgical procedure. The right leg was shaved and prepared using a standard antiseptic procedure. A 3 cm longitudinal cut lateral to the Achilles tendon was created on the skin. The Achilles tendon was identified by anatomizing the subcutaneous fat and surrounding soft tissues. The central portion of the Achilles tendon was transected and sealed end-to-end using a 5-0 Vicryl suture .The rats were arbitrarily divided into three groups . The animals in the normal group did not have any surgery, whereas the rats in the control group underwent surgery without any implantation. For the rats in the DBC nanofiber group, the Achilles tendons were circumferentially wrapped with DCB nanofibrous mats (1 cm × 1 cm) after the surgery. The injury was sealed with a 3-0 nylon suture . Bactericidal ointment was topically applied above the surgical wound to prevent infection. The rats were returned to their cages after complete recovery from anesthesia.After the operation, the level of activity of the rat was monitored daily by keeping each rat in a lab-assembled animal behavior cage  for seven days. Nine sensors  located at the top of the cage were used to monitor rat movements. Each sensor was triggered to record movement when the rat migrated from one area to another. The total triggered counts were recorded using a microprocessor and acquisition interface for seven consecutive days. Daily water and food intake were also recorded. Constant temperature (22 °C–25 °C), pressure (1 atm), and humidity (60–70%) levels were maintained for the entire observation period. After activity examination, the rats were returned to their initial cages for animal care.To evaluate the in vivo elution behavior of DCB-composite nanofibers, tissues surrounding the DCB-wrapped Achilles tendon were sampled on days 1, 3, 7, 14, 21, and 28 post-implantation with a surgical procedure similar to that for implantation. The drug concentrations in the collected samples were subsequently evaluated using HPLC.Tendons for evaluation were excised after the rats were euthanized. The cross-sectional diameters of the tendons where transection was performed were measured and recorded using a digital caliper.The retrieved Achilles tendons at 8 weeks post-implantation were evaluated using a Lloyd tensiometer with a 2.5 kN load cell . The tendons were first wrapped with gauze and submerged in a saline solution to mimic the in vivo environment. The Achilles tendon was stretched at a rate of 60 mm/min during testing.Wet tissues obtained from the investigated animals were trimmed and dehydrated using a serial alcohol solution. The dehydration sequence was 70% ethanol (15 min), 90% ethanol (15 min), 100% ethanol (15 min), 100% ethanol (15 min), 100% ethanol (20 min), 100% ethanol (30 min), and xylene (overnight) using a Shandon Excelsior . Tissue sections (3–5 μm) were collected using a microtome  for histological assessment. Furthermore, the acquired specimens were stained with hematoxylin and eosin (H&E), and the tenocytes and tissues were examined under a microscope with a magnification of 400.Immunohistochemistry (IHC) staining was performed on 4 μm paraffin sections of tendon tissues to document the expression of the bone morphogenetic protein (BMP-2), vascular endothelial growth factor (VEGF), von Willerdrand factor (vWF), transforming growth factor beta (TGF-b), and type I and III collagens using a BOND-MAX Fully Automated IHC and ISH Staining System . The antibodies used for IHC analyses included BMP-2 , VEGF , vWF , TGF-β , type I collagen , and collagen III . Heat-induced epitope retrieval was accomplished (95 °C/30 min) in a citrate buffer (pH = 6.0) after deparaffinization and rehydration. The slides were then incubated with a hydrogen peroxide solution (3%) for 5 min. After washing with the supplied buffer, tissue sections were repaired for 40 min using ethylenediaminetetraacetic acid. This was followed by incubating the slides with the primary antibody for 60 min at 37 °C and overnight at 4 °C. After three rinses in buffer, the slides were incubated with secondary antibodies. Tissue staining was performed using a 3,3′-diaminobenzidine substrate chromogen solution. To measure the collagen I and III in the specimens, the average optical density was processed using ImageJ software .t-tests, and differences were considered statistically significant at p < 0.05.Descriptive statistics are shown as the mean ± standard deviation. Data were analyzed using paired The fabricated nanofibers were characterized by field-emission SEM under 10,000× magnification . The aveThe water contact angles for the pristine and biomolecule-loaded PLGA nanofibrous mats were measured. The stress–strain curves of the spun nanofibrous mats were measured. The empirical data in The in vitro daily and cumulative drug-eluting quantifications of the electrospun doxycycline- and bupivacaine-loaded PLGA nanofibers are illustrated in The in vivo discharge behavior  suggestsp > 0.05).The rats were housed within the ABC to examine their postoperative bioactivities. p < 0.01). The experimental results demonstrate the nanofibers’ capability to repair ruptured tendons. This is mainly due to the larger diameter (5.2 ± 0.9 mm) of repaired tendons when compared to that of normal tendons (2.1 ± 0.4 mm), as shown in p < 0.05).HE staining revealed a regular organization of the collagens A. AbundaVarious treatments to repair Achilles tendon injuries beyond primary sutures have been reported ,18,19. Ap > 0.05).Tissue engineering has been demonstrated as an effective approach for treating ruptured tendons. Liu et al.  developeIn this study, DCB nanofibers were fabricated by integrating doxycycline, collage, and bupivacaine into PLGA nanofibrous membranes using an electrospinning technique. The experimental results illustrate that the strength of the repaired tendons is greater than that of normal tendons, demonstrating the superiority of DCB nanofibers over doxycycline-only nanofibers. Incorporating biomolecules into biodegradable nanofibers can stimulate various growth factors, and the injured Achilles tendon could possibly re-establish its biological composition and restore its mechanical strength.Doxycycline falls into the category of the tetracycline antibiotic family and shows excellent activity against Gram-positive and Gram-negative microorganisms . In addiPLGA nanofibers were acquired at a solution concentration corresponding to the creation of significant molecular chain entanglements in the polymers. The incorporation of collagen and pharmaceuticals reduces the content of PLGA in the solution and the solution viscosity. It becomes more difficult for the solution to resist the external electric force during electrospinning and the spun fiber size reduces accordingly.In general, the release of biomolecules from the degradable nanofibrous mats comprises three stages: a primary peak release followed by diffusion- and degradation-governed release. Most of the loaded biomolecules were embedded in the bulk of the PLGA nanofibers during electrospinning. Nevertheless, the pharmaceuticals loaded on the surface of the nanofibers cause a primary peak. After an initial burst, the discharge behavior is mainly controlled by osmotic diffusion and polymeric degradation. In our study, a second discharge peak of doxycycline and bupivacaine was observed on day 7. Subsequently, a gradually diminishing profile was observed. In contrast, in the in vitro discharge profile, a sustained release of the loaded pharmaceuticals was observed without a primary release peak because in vivo metabolism is generally slower than in vitro metabolism, and the in vivo primary peak release was reduced.Incorporating biomolecules into the PLGA nanofibers increased the wettability of the spun matrices, which in turn enhanced cell proliferation and tissue healing. Furthermore, the biomolecule-loaded PLGA nanofibrous mats had excellent extensibility and flexibility, demonstrating their feasibility as scaffolds to accommodate the extension/contraction of tendons in the therapeutic procedure.Another advantage of DCB–PLGA nanofibrous scaffolds is the pain-control capability provided by eluted bupivacaine. Postoperative pain, especially in orthopedic surgeries, remains a major concern. Although systemic administration or local injection of analgesic agents is employed as a routine procedure after surgery, it has drawbacks, such as inadequate bioavailability or systemic adverse effects. ,46 In thDespite the satisfactory outcomes of this study, there are some limitations. First, this animal study was conducted on normal Achilles tendons with sharp scalp-cut transections. However, a torn Achilles tendon in humans generally occurs in degenerated tendons after an unexpectedly distracted axial force. The tendon composition and healing process are slow or inadequate in degenerated torn tendons. Second, in vitro dose-dependent experiments on the loaded drugs were not performed. Further studies are recommended to elucidate the elution profiles of the drugs under different concentrations. Finally, the systemic concentrations of locally delivered drugs were not examined. Moreover, beyond the therapeutic effects of the drugs, side effects such as cardiac or renal involvement should also be assessed in the future.We successfully established tri-layered multi-biomolecule-loaded composite biodegradable scaffolds for the repair of ruptured Achilles tendons. Doxycycline, collage, and bupivacaine were integrated into PLGA nanofibrous membranes as healing promoters using an electrospinning technique, 3D scaffold, and painkillers, respectively. The DCB nanofibrous scaffolds provided a sustainable release of loaded pharmaceuticals both in vitro and in vivo for clinical applications. Tendons treated with nanofibrous scaffolds exhibited a significantly greater maximum load-to-failure than normal tendons. Additionally, the composition of the healed Achilles tendon possessed abundant tenocytes and collagen I. The DCB–PLGA scaffolds show great potential for treating Achilles tendons in humans.


Due to the subtle balance of intermolecular interactions that govern structure–property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fruitful approach involves classifying the different combinations of interacting chemical moieties, as understanding the relative energetics of different interactions enables the design of molecular crystals and fine-tuning of their stabilities. While this is usually performed based on the empirical observation of the most commonly encountered motifs in known crystal structures, we propose to apply a combination of supervised and unsupervised machine-learning techniques to automate the construction of an extensive library of molecular building blocks. We introduce a structural descriptor tailored to the prediction of the binding (lattice) energy and apply it to a curated dataset of organic crystals, exploiting its atom-centered nature to obtain a data-driven assessment of the contribution of different chemical groups to the lattice energy of the crystal. We then interpret this library using a low-dimensional representation of the structure–energy landscape and discuss selected examples of the insights into crystal engineering that can be extracted from this analysis, providing a complete database to guide the design of molecular materials. Due to the subtle balance of molecular interactions, predicting the stability of molecular crystals is a non-trivial scientific problem. Physically-motivated machine learning models can not only “rediscover” the maxims of crystal engineering, but also guide crystal design. In the past decades, there has been a growing push to develop a “holistic” view of molecular crystallization,14,15 not only taking into account the nearest-neighbor contacts but also the interplay of these interactions with other components of the molecular assembly.Yet, molecular crystallization is a complex process that involves multiple cooperative and competing forces. Initial nucleation is typically motivated by strong interactions between functional groups.14 Thus, molecular crystallization has emerged as a hotbed for computational inquiry. This focus has led to considerable theoretical and software developments for qualitative and quantitative analyses, including those tailored to crystal structure prediction (CSP)16–19 and the representation of electrostatic surfaces and molecular geometry.20,21 Even more recently, machine learning has been used to understand the individual configurational and energy landscapes of molecules;22–28 however, such techniques have yet to be applied in the general, holistic vein required to extract the qualitative insights that can be used to support crystal design efforts.While it is simpler to rationalize single-site interactions, the interplay of many competing interactions necessitates diverse, high-throughput studies.et al.29 In Cordova et al.,29 these crystals were initially selected by querying the Cambridge Structural Database (CSD) to identify a diverse set of synthesizable molecular assemblies, including some that had been experimentally stabilized at extreme conditions. The experimental properties of the full dataset are summarized in ESI Appendix A3.†To study molecular crystallization in this broad lens, we have curated a dataset of roughly 3260 C + H + N + O + S-containing molecular crystals from those reported in Cordova The stability of molecular crystals is traditionally studied through the binding (lattice) energy, which is computationally determined by evaluating the ground-state energies for both the crystal and its molecular components in the dilute gas limit, here computed using DFT-PBE-D2 calculations of each crystal and its relaxed molecular components at ambient conditions.31 with interactive components aimed to guide future molecular design and narrower or targeted studies.From here, we build an atom-centered regression model for this lattice energy, demonstrating the improvements in accuracy and reduction in model complexity from using a physics-informed approach. This atom-centered approach, wherein we represent each molecular crystal using the average ML descriptor for each of its atomic constituents, facilitates estimating the contribution of each atom, or group of atoms, to the binding energy. Then, employing a combination of supervised and unsupervised machine learning models, we can determine and interpret each molecular moiety's intermolecular interactions. Using these approaches, we show how physically-motivated machine learning models can not only “rediscover” the known maxims of crystal engineering, but provide insight and guidance for crystal design. We have made our datasets, and analyses openly-available through the Materials Cloud,232 to identify the contributions of specific collections of atoms to the binding of a crystal. Given the many atomic and energetic entities , we rely on many numerical representations and equations; hence we start by establishing a consistent notation we will use throughout the text.In this study, we employ atom-centered descriptors2.1x(i)σ, where the subscript σ signifies the collection of atoms being described, including the entire crystal (c), a molecule (m), or an atom (a). nσ is the number of atoms in the given collection. Thus it follows that nc is the number of atoms in a given crystal, and nm is the number of atoms in a given molecule. Because we discuss analogous atoms or molecules in both the solid and gas phases, we use the superscript (i) to denote the phase  or dilute gas (g)).To reflect the physics of atomic interactions, we use symmetry-adapted descriptors to encode/describe the geometric arrangement of atoms in their atomistic configurations, specifically the 3-body SOAP descriptors outlined in ESI Appendix B1.The descriptor for a given collection should be assumed as the average of the descriptors for the constituent atoms:. If we were to look at the same molecule in the crystalline solid we would get . A schematic of these concepts is shown in 30) as an example.For example, the descriptor for the atoms in a molecule in a dilute gas is 2.2Eσ to denote the total energy of a collection of atoms. In this study, the total energies of the crystals Ec are taken from those reported in Cordova et al.29 and the total energies of the molecules Em are determined by DFT-PBE-D2 calculations, as described in ESI Appendix A2.eσ ≡ Eσ/nσ to indicate the per-atom energy. Note that we express all energies in kJ mol−1, where eσ is to be interpreted as having the units of kJ per mol of atoms. Constructing a linear regression amounts to the ansatz.wσ is the regression weights and εσ the residual errors. The lattice energy  of a molecular crystal is given by Δc, whereWe use With the average lattice energy per atom given by,δa, where . We will also consider the contributions for different collection of atoms, and will denote the average lattice energy contribution as . When we regularize these contributions using a Gaussian filter  scale" fill="currentColor" stroke="none"><path d="M320 1080 l0 -40 -40 0 -40 0 0 -40 0 -40 40 0 40 0 0 40 0 40 80 0 80 0 0 -40 0 -40 80 0 80 0 0 40 0 40 40 0 40 0 0 40 0 40 -40 0 -40 0 0 -40 0 -40 -80 0 -80 0 0 40 0 40 -80 0 -80 0 0 -40z M400 840 l0 -40 -40 0 -40 0 0 -80 0 -80 40 0 40 0 0 -40 0 -40 40 0 40 0 0 -40 0 -40 -160 0 -160 0 0 -40 0 -40 -40 0 -40 0 0 -160 0 -160 40 0 40 0 0 -40 0 -40 120 0 120 0 0 40 0 40 80 0 80 0 0 40 0 40 40 0 40 0 0 160 0 160 -40 0 -40 0 0 80 0 80 -80 0 -80 0 0 80 0 80 80 0 80 0 0 -40 0 -40 40 0 40 0 0 40 0 40 -40 0 -40 0 0 40 0 40 -80 0 -80 0 0 -40z m160 -480 l0 -120 -40 0 -40 0 0 -40 0 -40 -40 0 -40 0 0 -40 0 -40 -120 0 -120 0 0 120 0 120 40 0 40 0 0 40 0 40 120 0 120 0 0 40 0 40 40 0 40 0 0 -120z"/></g></svg>.Later, we will use our regression model to determine the atomic contributions to the lattice energy, which we will denote 333 All models were trained on the same training set of 2707 crystals (or the corresponding 3242 molecules). We report errors on a mutually-exclusive set of 551 crystals (or the corresponding 628 molecules). When interpreting the results, it is important to consider that the test set has been selected at random, and is therefore representative of the makeup of the CSD, while the training structures were selected with farthest point sampling34,35 to maximize the diversity, and therefore contain a large fraction of unstable, “extreme” cases  by building a robust machine learning model on an atom-centered descriptor.3.1.1e with its approximation via linear regression ; therefore, the resulting RMSE of 0.916 kJ mol−1 from When we predict the crystal and molecular atomic energies 3.1.2x(g)c, while conceptually nonsensical (the gas-phase descriptors of the molecules contain no information on the intermolecular interactions), yields an RMSE of 1.101 kJ mol−1. Regressing on the solid-phase descriptors x(s)c improves the regression substantially, achieving an RMSE of 0.778 kJ mol−1.With the reduced variance of the target (lattice energy), it thus makes sense to construct the regression model directly on our target. Building a regression on the gas-phase descriptors x(s)c and x(g)c) contain the full set of relevant information – the molecular descriptor x(g)c is missing information on intermolecular interactions, and the crystal descriptor x(s)c is unaware of the conformational changes that the molecules undergo upon crystallization. The necessity of this missing information is confirmed when we regress on concatenated descriptors {x(s)c, x(g)c} and our RMSE drops to 0.671 kJ mol−1.Yet, conceptually, neither of these two representations (w = wc = wm. Substituting this into x(s−g)c ≡ (x(s)c − x(g)c) as the so-called “remnant” descriptor and ε again denotes the residual errors. Explicitly adapting our representation x(s−g)c to the nature of the lattice energy results in a yet better result to learning on {x(s)c, x(g)c}: 0.571 kJ mol−1, despite being in a smaller feature space. Conceptually, this descriptor still encodes the 3-body correlation between an environment and its neighbors but explicitly incorporates the change in molecular geometry upon crystallization and reduces the weights of atomic triplets whose interactions are primarily intramolecular and/or the same in gas and solid phase.Furthermore, 3.1.3x(s−g)c or taking the difference of non-linear feature vectors (see ESI Appendix C2−1 compared to the best linear model) by taking the difference of the non-linear features defined by the kernels of the crystalline and molecular descriptors. This result further emphasizes the rationale behind the remnant approach, and suggests that one can, more generally, improve accuracy by combining non-linear feature constructions to mimic the mathematical formulation of target properties. Kernel hyperparameter optimization has little impact on this conclusion, as we demonstrate with corroborating results using a parameter-free kernel, also in ESI Appendix C2.†This result is mirrored in non-linear regression models, where again, a superior result is obtained by either constructing a kernel on a priori, these results suggest that linear (summarized in x(s−g)c, rather than x(s)c, as has been common practice in the literature.23,25,27,28 Thus, in the remainder of the text, we will employ ML fingerprints and models based on the remnant descriptor.When the molecular geometry is known 3.23.2.1With our target-adapted regression model, we can assign effective contributions to each atomic environment, where we take the remnant descriptor of each atomic environment and computeδa. For the ith atom, this results in runs over all neighbors of i and  runs over all neighbors of j (defined by a cutoff of 2 Å). For neighbors a and b and interatomic distance dab, f = exp[dab2/2ς2]. This procedure, introduced for the electronic density of states in Ben Mahmoud et al.,37 has the effect of regularizing the decomposition without changing the regression results, i.e., . We show this effect of the filter on the distribution of atomic contributions in ESI Appendix B2.Despite the mathematical logic behind this step, the lack of physical underpinnings for this decomposition may result in energy being arbitrarily partitioned between neighboring atoms. This leads to disproportionately large contributions of opposite size being assigned, not dissimilar to how a regression may overfit by assigning large regression weights. To ease this effect, we can apply a Gaussian filter to each 3.2.238 and RDKit Substructure Matching39 to identify the atoms belonging to common molecular motifs, finding 46 010 motifs. Details of this procedure and our table of SMARTS strings are given in ESI Appendix B3 and Table S3,σ as§§The lattice energy of the crystal is not the sum of these motif contributions, as (1) both are averaged quantities, and (2) a single crystal may have overlapping motifs.Taking the 3242 molecules from our training set, we use SMARTS descriptorse.g., carboxyls and waters). As we move to the right, the molecular motifs are, on average, weakly binding, with the largest range of interactions coming from the most broadly-defined groups, including the alkanes, alkenes, and benzene-like rings.We plot the span of lattice energy contributions for motifs with greater than 200 instances in the dataset in 40 and adapted to chemical systems in Helfrecht et al.41 This technique produces a latent-space mapping that arranges different motif classes based on their structural similarity and correlation to a set of target properties. In 1) correlates strongly with the (learnable) cohesive interactions. The second axis (PCov2) allows us to resolve structural differences between motifs with similar energetic contributions. In this mapping, we can learn from the spread of each group. For example, the 868 water molecules , a hybrid supervised-unsupervised dimensionality reduction technique first introduced in De Jong and Kiers31,48The PCovR framework also provides a blueprint for analyzing the interactions of different structural motifs – given a single motif type, what characteristics of a molecular environment lead to a more stabilizing interaction? In the following sections, we will take a look at the stabilizing environments for a few classes of functional groups, starting with the well-known stabilizing interactions of water and carboxylic groups, then moving onto two groups with a wide range of intermolecular interactions, 6-membered aromatic carbon rings and nitro groups. With each functional group, we generate a new PCovR using only the averaged remnant descriptors and effective interactions for the instances of that group, such that the structural diversity embedded in the map reflects the diversity of interactions, rather than the diversity of the molecules. We have included similar maps for all other molecular motifs in an online data repository.3.2.3−1 , with the majority of interaction strengths occurring at around −24.65 ± 9.06 kJ mol−1. We generate a new PCovR shown in the left panel of a.We begin with a ubiquitous molecular crystal stabilizer: water. The estimated contributions of the 868 water molecules in this dataset span a range of −42.92 to 4.16 kJ mole.g., 46), with OH⋯O-type at the bottom of the map .First, we look at a common parameter for measuring the stabilizing effect of water: hydrogen bonding (H-bonding). Here, we have calculated H-bonds based on when the O⋯H or H⋯X distance is less than 2.5 Å and the dihedral angle of O⋯H–X or OH⋯X is greater than 150°. From the right side of i.e., the nature of the acceptor is primarily correlated with the second covariate).This analysis emphasizes that the number of hydrogen bonds does not fully capture all of the nuances of water stabilization – the majority of water molecules participate in 2–3 such interactions, and the energy of these bonds can span a wide range. In O⋯H–X interactions, there is little energetic difference based on whether the acceptor is a nitrogen or oxygen atom – both types of hydrogen bonds span the full range of energies. The nature of the acceptor is encoded in the covariate orthogonal to the chemical features most correlated with interaction strength  in a succinic acid molecule (56–58). Interestingly, this crystal also contains one of the most weakly interacting groups ethylene . Moving to the right, we see the contribution decrease commensurate to the number of interactions. For example, in 3,5-pyrazoledicarboxylic acid  show weak intermolecular interactions ranging from −11.27 kJ mol5960), where they stack above the carboxylic acid homosynthon.The most strongly-binding benzene-like motifs occur in molecules where (1) the ring is functionalized by strongly interacting groups, (2) the interactions of these groups facilitate planar molecular geometry, and (3) stacking occurs between the benzene-like rings with these auxiliary groups. We see this in 2,4,6-trinitrobenzene-1,3,5-triamine  to see how the nature of the same molecule changes based upon the interactions in the crystal. Both polymorphs adopt a similar herringbone crystal structure; however, the decreased molecular distortion and increased interactions between the auxiliary hydrogens and neighboring aromatic rings in PHENAN08 63 result in a significantly lower lattice energy of δc = −4.58. In 63 and PHENAN14 61 onto our PCovR map from 63 is an out-of-sample data point (ε = 0.2 kJ mol−1), demonstrating that the analysis in 63 crystal configuration in Fig. S5.†This is further supported by comparing the motifs of this polymorph with its ambient-pressure, stable counterpart , a molecule where the nitro group is strongly interacting with the CH3 end groups with some potential π-hole stacking71 between the nitrogen moieties, as shown in e.g., 67 KEDJUB68). In some of these cases, the binding is likely weakened by intramolecular interactions, similar to the contributions of the nitro groups in 2,4,6-trinitrobenzene-1,3,5-triamine -4-nitro-1,2,5-oxadiazole  or heterosynthons with a carboxylic acid group .We highlight the molecules that form the most similar acetamide networks to those in the ethenzamide dataset in 91), where the acetamide group forms a heterosynthon with the triazole group, and in O-carbamoylhydroxylamine (87), where the small size of the molecule facilitates both multiple homosynthons between acetamide groups, as well as heterosynthons with the oxygen of the hydroxylamine groups. In 2-oxopyrrolidineacetamide dihydrate (89), a network of hydrogen bonds is formed between acetamide groups and water molecules. In azidoacetamide (88), we see an acetamide homosynthon formed at an offset so that the azide group can stack directly above the NH⋯O interaction. We see weaker interactions in tetrazole-5-carboxamide (90), where the acetamide group is interacting with the azole group, which, when compared with triazole-5-carboxaldehyde (86), despite multiple acetamide interactions, there is a weaker acetamide network, likely due to the geometry of the molecule itself.Perhaps more interesting are the acetamide groups that form different interactions, highlighted in the bottom panel in aldehyde , demonstWe do not suggest that these molecules could be used directly as co-formers; the training set was obtained with diversity as the primary goal, with no regard for availability, toxicity, ease of synthesis, or stability. Instead, each of these related and unrelated crystals gives insight into the types of interactions that may beget new ethenzamide co-crystals. The molecules shown in 4Molecular crystallization is a complex, multi-faceted process, that poses tremendous challenges to both quantitative modeling, and to the derivation of qualitative design principles. In this work, we propose a data-driven strategy to build a database of the interaction motifs that are found in a diverse set of molecular crystals, to determine semi-quantitatively their contribution to the lattice energy, and to generate a library of molecular motifs that can be used to interpret the stability of known crystals and to assist the design of new ones.In doing so, we have to strike a balance between several conflicting goals. By selecting structures from the CSD with maximal structural diversity, we ensure that we cover a broad range of chemical and packing motifs, while remaining focused on structures that are known to be experimentally realizable. By using a general-purpose, atom-centered structural representation that is capable of describing arbitrary structural correlations, we ensure that our data analysis is flexible and that it does not incorporate pre-conceived notions about molecular bonding. At the same time, we ensure that the model focuses on the features that are most relevant to determine crystal stability by building a remnant descriptor that mimics the definition of the lattice energy as a difference between the total energies of the crystal and its constituents.−1 in predicting the atomic contributions to crystal stability using these descriptors that gives us a semi-quantitative estimate of the contribution of each atomic environment to the lattice energy and to compare between different co-crystals or between polymorphs that are stable at very different conditions. In order to translate these atomic contributions in a language that can be useful to crystal chemistry, we then assemble them to estimate the stabilizing power of traditional chemical groups  and build data-driven maps that facilitate the comparison of different chemical environments by expressing simultaneously the structural variability and correlation with the lattice energy contribution. For each chemical moiety we provide an interactive map  that allows to juxtapose different types of crystal environments, to identify structural patterns that are either stabilizing or destabilizing, and to contrast them with conventional motifs (e.g. hydrogen-bonding), demonstrated here for a few selected cases. As we demonstrate for phenanthrene, it is also possible to use these maps to compare polymorphs of the same molecule, and to analyze molecular motifs for a structure that is not part of our original reference set. With these tools, we aim to guide those designing molecular co-crystals in identifying suitable co-formers, as demonstrated for the analgesic ethenzamide.The resulting models achieve a respectable mean absolute error of about 0.4 kJ molWe hope that this library of molecular motifs will prove useful to applications to specific crystal-design problems. More broadly, we believe that the general ML protocol that we follow, combining regression of the ultimate target property with unsupervised analysis of molecular motifs, can inspire similar applications to the study of other classes of materials, ranging from metal and covalent organic frameworks to self-assembled monolayers and biological systems.https://molmotifs.matcloud.xyz. The raw data and visualization files are available at https://doi.org/10.24435/materialscloud:71-21.Data for this paper, including interactive visualizations and labelled molecular motifs, are available at MaterialsCloud at RKC and MC designed the study and wrote the manuscript. RKC computed the molecular energies and geometries, built the machine learning models, and designed the figures. MP separated the crystals into molecular components, screened the dataset before relaxation calculations, started the molecular energy calculations, and edited the manuscript. EAE advised on the dataset provenance and curation and edited the manuscript.There are no conflicts to declare.SC-014-D2SC06198H-s001


Neuronal activity in the brain is tightly regulated. During operation in real time, for instance, feedback and feedforward loops limit excessive excitation. In addition, cell autonomous processes ensure that neurons’ average activity is restored to a setpoint in response to chronic perturbations. These processes are summarized as homeostatic plasticity . In the basal ganglia, information is mainly transmitted through disinhibition, which already constraints the possible range of neuronal activity. When this tightly adjusted system is challenged by the chronic decline in dopaminergic neurotransmission in Parkinson’s disease (PD), homeostatic plasticity aims to compensate for this perturbation. We here summarize recent experimental work from animals demonstrating that striatal projection neurons adapt excitability and morphology in response to chronic dopamine depletion and substitution. We relate these cellular processes to clinical observations in patients with PD that cannot be explained by the classical model of basal ganglia function. These include the long duration response to dopaminergic medication that takes weeks to develop and days to wear off. Moreover, dyskinesias are considered signs of excessive dopaminergic neurotransmission in Parkinson’s disease, but they are typically more severe on the body side that is more strongly affected by dopamine depletion. We hypothesize that these clinical observations can be explained by homeostatic plasticity in the basal ganglia, suggesting that plastic changes in response to chronic dopamine depletion and substitution need to be incorporated into models of basal ganglia function. In addition, better understanding the molecular mechanism of homeostatic plasticity might offer new treatment options to avoid motor complications in patients with PD. Neuronal activity in the brain is tightly regulated. During operation in real time, for instance, feedback and feedforward loops limit excessive excitation. In addition, cell autonomous processes ensure that neurons’ average activity is restored to a setpoint in response to chronic perturbations. These processes are summarized as homeostatic plasticity (Turrigiano Parkinson’s disease (PD) is the second most common neurodegenerative disease. Its motor symptoms are caused by degeneration of dopaminergic neurons of the substantia nigra pars compacta (SNc) and deficient dopaminergic neurotransmission in the striatum. Accordingly, the classical motor symptoms of PD are alleviated by dopaminergic medications , suggesting that they represent an overshoot of the therapeutic effect , which are a model for dyskinesias. Similar to dyskinesias of non-human primates, rodent AIM require chronic dopamine depletion to occur Cenci . In addi“Tardive” dyskinesias are observed in patients after administration of dopamine receptor antagonists, suggesting that they result from similar adaptation processes as dyskinesias in patients with PD, with physiological dopaminergic neurotransmission taking the role of levodopa treatment  is the most studied form of neuronal adaptation. Striatal LTP is reduced in mice with dopamine depletion and restored by levodopa administration  of the subthalamic nucleus (STN) reduces PD motor symptoms to a similar extent as dopaminergic medication does. This raises the question whether STN-HFS, too, can elicit a LDR, or whether only dopaminergic medication can do that. One study showed worse motor performance in overnight medication withdrawal in patients with STN-HFS but without chronic dopaminergic medication (Wider et al.