SLC6A3 gene. Advances in genomic analysis have revealed an evolving spectrum of SLC6A3-related neurological and neuropsychiatric disorders. Since the initial clinical and genetic characterisation of DTDS in 2009, there have been thirty-one published cases with a variety of protein-truncating variants  and missense changes. Amino acid substitutions result in mutant proteins with impaired dopamine transporter function due to reduced transporter activity, impaired dopamine binding, reduced cell-surface expression, and aberrant posttranslational protein modification with impaired glycosylation. In this review, we provide an overview of the expanding clinical phenotype of DTDS and the precision therapies in development, including pharmacochaperones and gene therapy.Infantile parkinsonism-dystonia due to dopamine transporter deficiency syndrome (DTDS) is an ultrarare childhood movement disorder caused by biallelic loss-of-function mutations in the  The clinical spectrum of dopamine-related conditions is broad, encompassing movement disorders and neuropsychiatric diseases such as attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), addiction, and bipolar disorder . The dopDNAJC12 deficiency. These primary neurotransmitter disorders are associated with low cerebrospinal fluid dopamine metabolites [DNAJC6-related disease—one of many juvenile-onset forms of genetic parkinsonism—as well as numerous metabolic disorders, including the mitochondriocytopathies [Within clinical neurology, symptoms indicative of dopamine dysregulation include bradykinesia, tremor, hypomimia and dystonia, neuropsychiatric features , and cognitive impairment, as observed in Parkinson’s disease . Parkinsabolites . Childhoopathies ,6.SLC6A3, which encodes the dopamine transporter (DAT), in these two kindreds [SLC6A3, which encodes DAT .Dopamine transporter deficiency syndrome (DTDS) is a primary neurotransmitter disorder due to defective dopamine reuptake. Prior to gene discovery, the initial clinical description from 2004 reported three children presenting with infantile parkinsonism-dystonia and, paradoxically, raised cerebrospinal fluid (CSF) homovanillic acid (HVA) . In 2009kindreds ,9. SinceSLC6A3 variants revealed impaired dopamine transporter function due to reduced transporter activity, with impaired dopamine binding, reduced cell-surface expression, and aberrant posttranslational modification with impaired glycosylation [SLC6A3 alter expression, membrane localisation, hDAT density, dopamine reuptake activity, and dopamine neurotransmission dynamics, contributing to a pathophysiological spectrum from infantile to adult-onset parkinsonism-dystonia, termed DTDS. In addition, SLC6A3 variants are associated with neuropsychiatric diagnoses, including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and bipolar disorder [DAT regulates dopamine homeostasis by transporting extracellular DA into the intracellular space that controls the synaptic levels of DA in the mesolimbic and nigrostriatal pathways . In vitrsylation . The effsylation . DAT is disorder ,12,13,14SLC6A3 gene (OMIM # 613135). The detailed clinical characterisation of eleven children with DTDS established the classical features and progression of DTDS over time. Since 2011, there have been thirty-one DTDS patients reported in the literature [DTDS is a primary neurotransmitter disorder that presents with infantile parkinsonism-dystonia due to biallelic loss-of-function mutations in the terature ,22,23,24The classical DTDS phenotype typically manifests during early infancy with irritability, feeding difficulties, hypotonia, and delayed motor development ,9,15,25.With over a decade of experience in treating early onset DTDS patients, it is evident that there is limited response to standard pharmacotherapies ,15. PallSLC6A3. Such patients with later-onset atypical DTDS usually have a history of normal early neurodevelopment in infancy and early childhood (achieving independent ambulation and spoken language), presenting later in the second decade of life with tremor, progressive bradykinesia, dystonic posturing, and variable tone [Since the first description of early onset classical DTDS, there has been an expansion of the clinical phenotype, with the identification of patients with a milder, more slowly progressive condition associated with biallelic missense variants in ble tone . The firble tone . All thrble tone . Anotherble tone . Moleculble tone . In vitrble tone . For DATble tone . The lonSLC6A3 gene affect different functional parameters of the transporter, including gene expression, glycosylation required for membrane trafficking, substrate affinity, reuptake activity, and transport direction. These differing facets of aberrant transporter function influence DA neurotransmission and contribute to a spectrum of neuropsychiatric DAT-related disorders that include ASD, ADHD, and bipolar disorder , transporter mislocalisation with reduced cell-surface expression, and subsequent impairment of transport activity . It is tPrevious studies have shown that chloroquine inhibits lysosomal activity and limits DAT lysosomal degradation . For DTDSLC6A3 gene to treat causative biallelic loss-of-function mutations. There have been two studies evaluating viral vector-mediated gene supplementation for DTDS. A proof-of-concept study delivered two AAV vectors into the midbrain of adult DAT knockout mice by stereotactic injection [SLC6A3 into the lateral ventricles of DAT knockout mice. This was well tolerated and rescued the survival, motor phenotype, and neurotransmitter profile. However, at high dosage, off-target DAT expression was associated with neurotoxic effects [Viral gene therapies deliver therapeutic genes to affected cells, using modified viruses to transduce target cells and deliver a therapeutic gene. The main viral vectors utilised clinically for monogenic disease are adeno-associated virus and lentiviral vectors . These tnjection . To achinjection . Howevernjection . Treatin effects . For cli effects . Our stuSLC6A3, encompassing DTDS parkinsonism-dystonia, early onset parkinsonism, and neuropsychiatric diseases (ADHD and ASD). Over the last decade, we have now identified 51 patients with DTDS. The ability to model disease-causing missense mutations in vitro has provided significant mechanistic insight. Our experience of evaluating DTDS patients confirms that the currently available treatment options are inadequate and palliative at best. Significant efforts to develop disease-modifying novel therapeutics for DTDS patients are now needed, and it is reassuring to know that approaches exploring the potential utility of novel pharmacochaperones and translational viral gene therapy approaches are underway. With the advent of new disease models such as 3D organoid modelling, it will be possible to better represent the brain microenvironment with various neuronal cell types and mimic the complexity of brain homeostasis in disease states to evaluate the effects of DAT dysfunction in human neuronal networks (Since the first clinical report of infantile parkinsonism-dystonia with paradoxically raised dopamine metabolites, there have been profound advances in understanding DTDS. Advances in genomic analysis have transformed the diagnostic process for these children; the identification of the causative gene has facilitated the recognition of a distinct clinical syndrome, whilst increasing diagnostic access to next-generation sequencing technologies has prompted earlier diagnoses . Such genetworks . Such hu


Antimicrobial resistance (AMR) is a critical global health threat, and drivers of the emergence of novel strains of antibiotic-resistant bacteria in humans are poorly understood at the global scale. We examined correlates of AMR emergence in humans using global data on the origins of novel strains of AMR bacteria from 2006 to 2017, human and livestock antibiotic use, country economic activity and reporting bias indicators. We found that AMR emergence is positively correlated with antibiotic consumption in humans. However, the relationship between AMR emergence and antibiotic consumption in livestock is modified by gross domestic product (GDP), with only higher GDP countries showing a slight positive association, a finding that differs from previous studies on the drivers of AMR prevalence. We also found that human travel may play a role in AMR emergence, likely driving the spread of novel AMR strains into countries where they are subsequently detected for the first time. Finally, we used our model to generate a country-level map of the global distribution of predicted AMR emergence risk, and compared these findings against reported AMR emergence to identify gaps in surveillance that can be used to direct prevention and intervention policies. AMR bacterial strains have been associated with increased mortality, longer illnesses, medical complications in surgery, barriers to chemotherapy and higher healthcare costs –4. GlobaStaphylococcus aureus, have been reported from livestock, wildlife and environmental samples ), doubling consumption predicts a 39% (±56%) increase in AMR emergence rates  effect of livestock antibiotic consumption was negatively associated with AMR emergence rates . For a country in which AMR emergence is expected (i.e. non-zero prediction), a 22% (±44%) greater than average AMR emergence rate is expected at twice the average inbound tourism (mean inbound tourism per capita = 1.3), with all other variables held at average. Migrant population, normalized to human population size, was not associated with the outcome. The dollar value of antibiotic exports, normalized to human population size, was inversely associated with AMR emergence rates . Healthcare expenditure as a percentage of GDP was also inversely associated with AMR emergence rates .Inbound tourism volume per country, normalized to human population size, was positively associated with AMR emergence rates . Conversely, the number of per capita ProMED reports related to a country (ProMED mentions) was positively associated with a country's zero-reporting probability , and ProMED mentions were not associated with emergence rates. Neither English-speaking in a country nor the publication bias index were associated with reporting of AMR emergence events or with AMR emergence rates.We compared different variables as proxies for reporting bias in AMR reports. Human population size was negatively associated with a country's zero-reporting probability ; (ii) imputation of either human or livestock antibiotic consumption (n = 74); (iii) imputation of human and livestock antibiotic consumption for countries within GDP range of countries with complete human and animal antimicrobial consumption data (n = 95); and (iv) full imputation (n = 193). While IRR and OR differed among the models, the overall direction of effects was largely consistent, and interpretation did not vary drastically between models . Human antibiotic consumption was positively associated with AMR emergence rates in all models except for the no-imputation model , which included only 36 of 68 available human antibiotic consumption data points. The effects of livestock antibiotic consumption, GDP, and their interaction were the same across models except for the full imputation model , which imputed 152 of 193 of livestock antibiotic consumption data points. Here, we report results from the third model—imputation of human and livestock antibiotic consumption for countries within the GDP range of countries with complete human and animal antimicrobial consumption data. We selected this model because it maximizes data coverage without predicting beyond the conditions of the observed data. Results for the other models are reported in the electronic supplementary material.Due to lack of data on human and livestock antibiotic consumption for many, especially low-income countries , we used model-imputed values for these correlates. To test robustness of results, we evaluated results under four imputation scenarios: (i) no imputation of antibiotic consumption (Model 3.1).To ensure robustness of our results, we tested various model formulations. Given that the USA is a singular outlier in metrics such as the number of reported events, GDP, publication bias index, ProMED mentions and antibiotic exports, we conducted a separate model analysis excluding the USA. In this model, neither livestock antibiotic consumption nor the interaction of livestock antibiotic consumption with GDP were associated with the AMR emergence rates, although the direction of the effects remained the same . Finally, following Jones et al. –$111 542 per capita [Luxembourg]), and therefore our results should not be extrapolated beyond this range. Finally, it is important to emphasize that the relationships discussed in this paper are associative, and causality can only be hypothesized through this type of global analysis. Further work on the mechanisms of what drives the origin of new strains, and what drives their maintenance, amplification and spread is urgently needed.There are several limitations to this study. First, it analyses trends in novel AMR strains reported in the literature from 2006 to 2017 against data collected on antibiotic usage in humans and animals from specific years . Variation in antibiotic usage in the 11 years of AMR reporting could cause confounding if countries have reduced or modified antimicrobial use (e.g. banning antibiotics as growth promoters in livestock). Second, it uses published data on novel AMR strains. While we included several measures of reporting and publication bias, the changes in interest or capacity to diagnose and identify AMR over this period may have varied among countries irrespective of economic capacity, due to trends in research fields. Third, data availability of some of the correlates is skewed to richer countries. Livestock antibiotic consumption data, estimated from country-reported antibiotic sales from livestock  – $111 542 percapita [Luxembourg]), which have both human and animal antimicrobial consumption data (n = 95). (4)Full imputation—Includes all countries in the dataset (n = 193).The following remaining variables were unavailable for some countries, with a distinct bias of missing data in low-income countries: human antibiotic consumption, livestock antibiotic consumption, health expenditure and inbound tourism. We imputed missing values for these variables, using four approaches to check for robustness:We used a multivariate imputation by chained equations (MICE) algorithm with classification and regression trees (CARTs) to model missing values based on the available data . CARTs a (c)n = 95). First, because the USA is a singular outlier in the number of reported events, GDP, publication bias index, ProMED mentions and antibiotic sales, we ran a model with the USA removed. Second, we used an alternative scaling of livestock antibiotic consumption, replacing per-human-capita livestock antibiotic consumption with per-livestock biomass antibiotic consumption. In this formulation, we also included livestock biomass, defined as the total mass of cattle, pig and chicken populations within a country [global emergence of antibiotic strains, rather than first national emergence. In this formulation, a drug–bacteria combination is counted only in the single country in which it first emerged in our dataset.We tested several alternative formulations of our model to determine robustness of results. We tested several alternative formulations of our model to determine robustness of results, using model 3 for missing data handling per capita rate.We used a Poisson-hurdle model, in which the response is a function of two components: a zero-inflated logistic component representing the probability of a country reporting zero events, and a Poisson component of the number of AMR emergence events in the period, conditional on observed reporting. For the logistic equation, we included our five reporting bias variables . All variables were included in the Poisson component, with human population size treated as an offset variable to estimate AMR emergence as a t-distribution  for all coefficient priors [We took a Bayesian approach to estimate model parameters, using a No-U-Turn Hamiltonian Monte Carlo implemented in Stan  and assut priors ,39. We uWe visually examined Markov chain trace plots and used effective sample convergence statistics ({\hat{R}} convergence less than 1.05) to confirm convergence across chains . We comp (e)n = 95) using only the Poisson component of the model, assuming a zero-inflated (logistic) probability of 0 for all countries . By removing the logistic equation from the hurdle model, we were able to estimate AMR emergence counts corrected for excess zeros due to underreporting. We used a sample of 500 β coefficients generated from our model for each variable to be able to produce median and 95th percentile count estimates for each country.We calculated reporting-corrected AMR emergence count predictions for all countries in our main model dataset ( (f)Data analysis was performed in R version 4.2.1 , using t


Orthopedic surgery literature utilizes numerous eponyms, and they have become commonplace among orthopedic surgeons and the general public alike. These eponyms can have important historical implications and their history is often overlooked by the physicians using such terms. This paper seeks to specifically explore the origins of eponyms in orthopedic soft tissue diseases involving the upper extremity. Shedding light onto the origin of these eponyms can provide greater respect and understanding of their use in orthopedic surgery today. Modern orthopedic literature is filled with eponyms named after pioneers in the field. From fractures to diseases to diagnostic tests, eponyms are a path for surgeons to cement their legacy in the orthopedic vernacular. Despite the frequency of eponyms in orthopedics, modern curriculums do not stress the origins of these terms, thereby alienating the learner from orthopedic history. By studying the eponymous surgeons and scholars, and forming an understanding of the “why” or “how” something came to be, one is able to have a more complete picture with which to frame their understanding of orthopedic problems. This paper seeks to specifically explore the origins of eponyms in orthopedic soft tissue diseases.Eponymous soft tissue diseasesDe Quervain’s TenosynovitisDe Quervain's tenosynovitis is a medical condition named after Fritz de Quervain, a Swiss physician born in Sion in 1868 [De Quervain was the first physician to describe and treat chronic stenosing tenosynovitis at the radial styloid process . He publDupuytren’s ContractureThe Lancet in 1834 [Dupuytren’s contracture, also known as Dupuytren’s disease, is named after the French surgeon Baron Guillaume Dupuytren . He firsThere has been historical debate about whether the eponym should be named after Dupuytren, as two surgeons proposed treatments for the palmar contractures before him (Henry Cline Sr. and Astley Cooper) . The terDupuytren’s disease is characterized by an abnormal thickening of the fascia, which can develop into a thick cord that prevents one or more digits from contracting . The fouVolkmann ContractureHandbook of Basic and Special Surgery, where he first described the contracture that now bears his name [Richard von Volkmann, born in Leipzig, Germany in 1830, was a professor of surgery at the University of Halle from 1867 . He attehis name . He reitToday, Volkmann's contracture, also known as Volkmann's ischemic contracture, is defined as a permanent flexion contracture of the hand at the wrist, resulting in a claw-like deformity of the hand and fingers .Apert SyndromeApert syndrome is named after French pediatrician Eugène Charles Apert. This is one of the several congenital abnormalities with parallels in the animal kingdom . Born inWartenberg’s Sign and SyndromeRobert Wartenberg, a neurologist, was born in Grodno, old Lithuania (present-day Belarus), in 1887 [In 1932, Wartenberg described five cases of mononeuropathy of the superficial branch of the radial nerve, which he named "cheiralgia paresthetica" or hand pain [In addition to the eponymous syndrome, Wartenberg’s name is affixed to a sign of ulnar nerve palsy . This siVaughan-Jackson SyndromeVaughan-Jackson syndrome is a specific pathophysiologic process that leads to extensor tendon rupture in a rheumatoid wrist. Vaughn-Jackson syndrome was originally defined in 1948 by Dr. OJ Vaughan-Jackson, who described a unique set of two patients that had extensor tendon rupture due to attrition at an arthritic distal radio-ulnar joint . He descPatients with Vaughan-Jackson syndrome typically experience a sudden loss of finger extension starting at the fifth digit and progressing radially toward the second digit . TherefoEponyms are often a frequent source of frustration for the orthopedic learner, providing little connection to the underlying disease, or evaluation to which they belong. Yet, they provide an opportunity to understand the greater context of orthopedics and the pioneers who came before us. Connecting with this history allows practitioners to learn from the past to develop a greater understanding of present problems. Regardless of the degree to which eponyms persist in the orthopedic vernacular or education, what ought to persist is the history the eponym carries with it.


Acetylation could improve the bioavailability of (-)-Epigallocatechin-3-Gallate (EGCG), but the relationship of substitution degree and antioxidant capacity of acetylated EGCG was unclear. The acetylated EGCG products were separated by preparation high performance liquid chromatography (HPLC). Two mono substituted acetylated EGCG, three substituted acetylated EGCG (T-AcE), eight substituted acetylated EGCG (E-AcE) and (-)-Epigallocatechin gallate (EGCG) were isolated. The 7-acetyl-EGCG (S7-ACEGCG) and 7-acetyl-EGCG (T-AcE) were identified for the first time. The antioxidant capacity, superoxide anion radical scavenging capacities, and hydroxyl radical scavenging capacities of EGCG decreased significantly after acetylation modification. The more EGCG acetylation modification sites, the lower the total antioxidant capacity, superoxide anion radical scavenging capacities, and hydroxyl radical scavenging capacities. The antioxidant capacity, superoxide anion radical scavenging capacities, and hydroxyl radical scavenging capacities of 5-acetyl-EGCG (S5-ACE) were higher than 7-acetyl-EGCG (S7-AcE). Combining all the results in this and previous studies, acetylation modification is not conducive to the performance of EGCG antioxidant capacity. However, due to the hydroxyl structure of EGCG, the further applications of natural EGCG are restricted, which is the poor lipid solubility, unstable in vivo, low absorption and bioavailability8. The molecular modification studies showed that the physicochemical properties and biological activity, e.g. anti-aging, antibacterial, anti-cancer of EGCG, could be changed by acylation, esterification, etherification, glycosilation16. The acetylation molecular modification can effectively improve the bioavailability and biological activity of EGCG19.Epigallocatechin gallate (EGCG) is the key bioactive component of tea catechins. Numerous studies showed the beneficial activities of EGCG, including anti-aging, antioxidant, antibacterial, anti-cancer, and so on20. The acetylated EGCG moleculars modified with different degree of substitution are formed by the O-acetylating substitution reaction. Enzymatic synthesis, chemical synthesis, ultrasound-assisted, microwave-assisted techniques are widely used in the preparation of functional active substances29. The catechins could been catalyzed by lipase at 45 ℃ for 48 h, and different degrees of acetylcatechins were obtained after purification with silica gel column30.EGCG has eight active hydroxyl sites, and could be replaced by acetyl groupDue to different substituent sites, the chemical characteristics of acetylated EGCG moleculars are also different, such as quantities, space conformation, the anti-cancer, anti radiation, and immuno-enhancement activities. However, the antioxidative activities of different acetylated EGCG moleculars are not clear yet. In this paper, TLC, HPLC/MS and NMR techniques were used to analysis the relationship of substitution degree and antioxidant capacity of acetylated EGCG.EGCG (> 98%) was supplied by China wuxi taiyogreenpower Technology Co., Ltd. Acetonitrile (chromatography-grade solvents) and methanol (chromatography-grade solvents) were obtained from Germany MERCK Corporation. Ethyl acetate, acetic anhydride and pyridine, ethanol, acetic acid, methylene chloride, sodium bicarbonate, sodium sulfate, sodium chloride obtained from China Beijing Dingguo changsheng Biotechnology Corporation.The EGCG sample (1.0 g) was placed into a reactor tube (500 mL). 100 mL of ethyl acetate solution, 2.0 mL acetic anhydride and 1.0 mL of pyridine were added. The reactor tube was put on the magnetic stirrer . The rotate speed was 400 r/min. The rotate time was 3 h. The 10 mL distilled water was added to stop the reaction. It was allowed to wash by water saturated sodium bicarbonate and saturated sodium chloride. The organic layer was collected and dried with anhydrous sodium sulfate. The coarse EGCG acetylation modified moleculars samples were obtained. Samples were separated by TLC . 4.2 g target coarse sample was obtained.Coarse sample was separated on an preparative HPLC system  equipped with a pump, an auto sampler, 2487 Dual UV detector and M701 Fraction Collector. Sample was injected into a Sunfire RP-C18 column . The column temperature was controlled at 35 ℃. Ultra pure water and acetonitrile were used as eluents. The flow rate was 25 mL/min. The detector was set to 280 nm. The following elution profile was used: 0 ~ 100 min, liner gradient from 95 to 65% (v/v) B. The eluant were collected at each branch pipe 20 mL. Fr. High Performance Liquid Chromatography (HPLC) analysis method: HPLC system  equipped with a pump, an auto sampler, 2695 detector. Fr. Electrospray ionization-mass spectrometry (ESI–MS) analysis method: Fr. The antioxidant activities of Fr. Statistical calculations were performed using the SPSS 13.0 for windows release computer program. Results were expressed as mean ± standard deviation (SD). The paired samples two-tailed T-test was applied for the comparison between two groups. Significance was defined as P < 0.05. The statistical analysis of the antioxidant activity data between the means was carried out by one-way analysis of variance (ANOVA) using SPSS statistics 26.0, followed by Duncan’s multiple range test, to compare the means for significant variation (p < 0.05).The Fr. 5. But the structure elucidation of mono- acetylated EGCG have not revealed. In this study, The infrared spectroscopy data of (a) SoEGCG was following: 3180 (–O–H), 1754 (–C=O), 1673 (–C=C), 1457, 1322 cm–1  –EGCG was following: 8.61 (C-2), 69.17 (C-3), 26.99 (C-4), 157.20 (C-5), 96.51 (C-6), 151.41 (C-7), 95.86 (C-8), 130.29 (C-1’), 106.81 (C-2’), 146.68 (C-3’), 133.90 (C-4’), 146.68 (C-5’), 106.81 (C-6’), 121.26 (C-1’’), 110.24 (C-2’’), 146.30 (C-3’’), 139.58 (C-4’’), 146.30 (C-5’’), 110.24 (C-6’’), 167.48 (C=O), 20.58 (AC-Me), 170.78 (AC-C=O). The 1H-NMR result of 7-Acetyl-(-) -EGCG was follows: δ2.95 , δ6.33 , δ6.23 , δ6.50 , δ6.92 . Based on the results of 1H-NMR and 13C-NMR spectra, the molecular formula of 5-Acetyl- (-) -EGCG is C24H18O12  was 507.4 m/z. [2 M + Na] + peak data was 1091.8 m/z. MS- base peak data (molecular ion peak [M–H]–) was 483.4 m/z. [2 M–H]– peak data was 1067.8 m/z. The molecular weight of EGCG was 458.4. After acetylation, the three -OH site (molecular weight 17) of the EGCG molecular structure was substituted by three acetyl groups (molecular weight 129). That is to say, the molecular weight of three acetylated EGCG was exactly 484.4 m/z, which was also the molecular weight of Fr.  –1 Fig. b.13C-NMR result of Fr.3 was following: 77.47 (C-2), 71.04 (C-3), 26.30 (C-4), 98.88 (C-5), 96.98 (C-6), 157.93 (C-7), 158.17 (C-8), 135.66 (C-1’), 120.30 (C-2’), 144.70 (C-3’), 138.21 (C-4’), 144.70 (C-5’), 120.30 (C-6’), 129.08 (C-1’’), 123.17 (C-2’’), 144.85 (C-3’’), 140.25 (C-4’’), 144.85 (C-5’’), 123.17 (C-6’’), 164.92 (C=O), 20.40 (2′ 3′ 4′ 5′ -Acetyl), 169.37 (Ace-C=O), 19.98 (3′ 4′ 5′ -Acetyl), 168.13 (Ace-C=O), 20.45(2″ 3″ 4″ 5″ -Acetyl), 169.56 (Ace-C=O). The 1H-NMR result of Fr.3 was follows: δ5.63 , δ5.23 , δ3.07 , δ5.99 , δ6.00 , δ7.28 , δ7.61 , δ7.61 , δ7.61 , δ7.61 . In this study, the infrared spectroscopy data of Fr. –1  was 817.4 m/z. [2 M + Na] + peak data was 1611.8 m/z. MS- base peak data (molecular ion peak [M–H]–) was 793.4 m/z. [2 M–H]– peak data was 1587.4 m/z. The molecular weight of EGCG was 458.4. After acetylation, the eight hydrogen atoms (molecular weight 8) of –OH site of the EGCG molecular structure was substituted by three acetyl groups (molecular weight 336 m/z). The molecular weight of eight acetylated EGCG was exactly 794.4 m/z, which was also the molecular weight of Fr.  –1 Fig. c. Based 3+ to Fe2+, which combine with phenanthroline for forming a stable complex. The active antioxidant group in molecular structure of EGCG is eight hydroxyl groups, which can not only remove hydroxyl free radicals and reactive oxygen species, decompensate peroxides, block the peroxidation chain, but also can coordinate with metal ions in order to remove metal ion catalysis. The total antioxidant capacities of EGCG, SoEGCG, ToEGCG and AcEGCG are shown in Fig. The total antioxidant capacity kit is mainly to determine the antioxidant ability by restoring FeThe scavenging superoxide anion radical capacities kit is mainly to determine the antioxidant ability by analyzing the reduction on superoxide anion, which generate in the simulated reaction process between medium yellow purine and xanthine oxidase. It is the customary method for antioxidant activity evaluation. The scavenging superoxide anion radical capacities of EGCG, SoEGCG, ToEGCG and AcEGCG are shown in Fig. Scavenging hydroxyl free radical capacity kit, which measures the inhibition degree of absorbance decreasing at 536 nm by samples, is the customary method for antioxidant activity evaluation. The scavenging hydroxyl free radical capacities of EGCG, SoEGCG, ToEGCG and AcEGCG are shown in Fig. The preparative liquid chromatography, LC/MS, NMR methods were used to isolate and identify the EGCG acetylating products in this study. The single substituted O-acetylated epigallocatechin gallate (SoEGCG), three substituted O- acetylated EGCG (ToEGCG), and peracetylated EGCG(E-AcEGCG) were isolated. 5-Acetyl-(-)-EGCG and 7-Acetyl-(-)–EGCG were identified. Separation methods in this study, provide the reference for further separation system of EGCG acetylated product, and obtaining different substitution degree of EGCG acetylating components. There are eight hydroxyl groups in the molecular structure of EGCG, which of substitution site contain O-substitution site and C-substitution site. The molecular structure, configuration, conformation and other aspects of different substitution degree of EGCG acetylating components were not clear.17. The peroxide value (POV) and P- anisidine value (AV) index were emploied to evaluate the antioxidant activity of EGCG and acetylated EGCG mixture5. The results of acetylation of EGCG by Zhu indicated that the antioxidant activity of acetylated EGCG mixture was lower than EGCG9. The lipophilicity of the tested compound was in the order tri-acetylated derivatives > di-acetylated derivatives > mono-acetylated derivatives18. Comparing with the previous studies, we found that the total antioxidant capacity, scavenging superoxide anion radical capacity, scavenging hydroxyl free radical capacity of SoEGCG were also significantly lower than EGCG. But the scavenging superoxide anion radical capacity of ToEGCG was significantly higher than EGCG. And the total antioxidant capacity of ToEGCG was significantly higher than EGCG. The scavenging superoxide anion radical of SoEGCG was higher than ToEGCG.The antioxidant activity of different EGCG acetylating components were different, which were affected not only by the substitution degree, but also by the substitution site. By comparing the total antioxidant activity of peracetylated EGCG and EGCG, it was found that the antioxidant activity of peracetylated EGCG was significantly lower than that of EGCGThe possible reason is that the antioxidant activity is not only related to the number of substitution group, but also the space conformation. Though the poor solubility, easy conversion, low bioavailability of EGCG in utilization can be solved by acetylating, antioxidant activity of different EGCG acetylating components are influenced by different factors, e.g. stereo chemical structure, configuration. The influence mechanism still needs to be further discussed.Supplementary Information.Supplementary Figure S1.


Retinal diseases, including a wide variety of diseases, such as age-related macular degeneration, diabetic retinopathy, retinal detachment, epiretinal membrane, and retinitis pigmentosa, can be caused by diverse pathogenic processes, such as aging, angiogenesis, infections/inflammation, trauma, drug-induced toxicity, and genetic mutation. Visual impairment secondary to retinal diseases is a global public health problem that can be a significant economic burden to society due to the loss of labor productivity. Therefore, early diagnosis and appropriate management of these diseases are required.Advances in clinical imaging have enabled visualization of the retina at unprecedented resolution. For example, optical coherence tomography (OCT) provides non-invasive, quasi-histological cross-sectional images of the retina, which have revolutionized the diagnosis of retinal diseases. However, most imaging modalities detect the disease after the structural changes caused by diverse pathological processes become apparent. Early detection of retinal diseases is an unmet clinical need, which may allow for early intervention and better outcomes. In this light, molecular imaging has provided a window into dynamic pathogenic processes using molecular biomarkers in human diseases . By visuRecently, advances have been made in the field of molecular imaging of retinal diseases. These have shown promising results for the early detection of retinal diseases. For example, several retinal endothelial cell biomarkers may visualize choroidal neovascularization, a hallmark of age-related macular degeneration, at a stage before which it can be detected using conventional imaging, such as fluorescein angiography –5. AccorFurthermore, artificial intelligence (AI) is increasingly being applied to retinal diseases, and retinal image analysis using AI has demonstrated significant potential for the screening of retinal diseases, such as diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity –8. This Papuaga et al. generated a novel optical imaging probe to visualize NLRP3 inflammasomes in choroidal neovascularization lesions and proposed that the probe may be a useful molecular imaging tool to monitor the onset, progression, and therapeutic response of age-related macular degeneration. Jeon et al. demonstrated the imaging of retinal CX3CR1-GFP+ cells, which were useful in analyzing the behavior of inflammatory cells after arterial recanalization in an animal model of branch retinal artery occlusion. Yang et al. developed a novel knockout model of retinitis pigmentosa using Pde6b-knockout Long–Evans rats, which showed characteristic retinal features. Serial fundus images and OCT of Pde6b-knockout Long–Evans rats showed bone spicule pigmentation and progressive retinal thinning, respectively.In this Research Topic, three original research articles report the development and verification of molecular imaging techniques or animal models of retinal diseases. In a mouse model of age-related macular degeneration, Kuang et al. developed segmentation methods for retinal vessels, which can be important biomarkers for systemic diseases . The methods combined unsupervised methods of local phase congruency and orientation scores with the deep-learning network modified from U-Net, which resulted in superior performance compared with conventional methods, particularly for small vessel segmentation. Finally, a review article by Yusuf et al. summarized novel retinal imaging techniques potentially applicable to screening hydroxychloroquine-induced retinal toxicity. The techniques included en-face imaging, topographic thickness maps, sequential retinal thickness analysis, fluorescence lifetime imaging ophthalmoscopy, near-infrared fundus autofluorescence, and quantitative autofluorescence. Better or earlier detection of the disease at the preclinical stage using novel imaging techniques and AI-assisted diagnostics may further reduce the risk of visual loss due to retinal toxicity or other causes.In summary, this Research Topic focused on the identification of novel biomarkers and advances in molecular imaging techniques for retinal diseases. These articles highlight progress in the current understanding of the pathogenesis of several common retinal diseases and the potential of molecular imaging techniques for earlier diagnosis of the diseases. These studies provide insights into future translational research and clinical applications of molecular imaging with the final aim of improving the outcome of the disease by resolving the unmet needs of early detection.The author confirms being the sole contributor to this study and has approved it for publication.


The government of Uganda, through its Ministry of Health, previously adopted curriculum review as a mechanism to respond to public health threats such as HIV/AIDS and include content in primary and secondary schools. This approach contributes to raising public awareness, a key strategy recommended by the World Health Organization to support the global response to the threat of antimicrobial resistance (AMR). This policy brief, developed for policymakers related to school curricula, aims to advocate for and support integration of AMR content in Uganda's primary and secondary level school curricula. The policy brief supports efforts by the multisectoral National AMR Subcommittee to create awareness on this issue as part of its role in facilitating the operationalization of Uganda's National Action Plan on AMR. Educational curriculum reviews are an innovative and effective approach for responding to public health threats by, for example, including topics related to TB and HIV/AIDS –6. PriorThe World Health Organization (WHO) Global Action Plan on antimicrobial resistance (AMR) recommends that countries include AMR and antimicrobial-use topics in school curricula in order to promote better understanding and awareness of the issue and provide the public with accurate and relevant information . WHO hasIn this policy brief, we recommend considering the inclusion of at least some basic AMR-related content for all levels of school education in Uganda, specifically primary and secondary levels. We provide context regarding the burden of AMR in Uganda and its linkage to the wider global burden of drug-resistant infections and efforts to combat the problem. We then provide an AMR-related policy context for Uganda, specifically focusing on WHO's Global Action Plan and Uganda's National Action Plan on AMR (NAP-AMR) and National Action Plan for Health Security , 14, 15.Staphylococcus aureus (MRSA) and those that produce extended-spectrum beta-lactamase, is on the rise, and these pathogens are displaying increasing resistance to even antibiotics that are generally reserved for tough multi-drug resistant infections  education comprising 4 years of lower secondary also known as ordinary level (O-level) and 2 years of upper secondary school also known as advanced level (A-level); and finally, 3 to 5 years of post-secondary education .The curricula are developed by the National Curriculum Development Center (NCDC) of the Ministry of Education and Sports (MOES). All schools are expected to follow the same curricula, with any additional external curricula requiring approval from the National Curriculum Development Center and MOES before implementation .Analysis of the Uganda education and training curricula for AMR and related content, using a set of keywords, was conducted in August 2022 to understand the scope and depth of the content covered and its relevance to AMR containment efforts. The exercise, coordinated by Makerere University Biomedical Research Center, involved key stakeholders, including the National Curriculum Development Center, MOES, and health professional councils. This keyword analysis technique was employed to examine and analyze textual data, allowing for the identification and exploration of specific keywords and key phrases within curricula documents to gain insights, detect patterns, or extract relevant information related to AMR content . The curBox 1Resistance to antimicrobialsMicroorganisms/germs/bacteriaAntimicrobial/antibiotic resistanceSensitivityPesticides/insecticideInfection Prevention and ControlInfection/infectious diseasesWater borne diseases/zoonosesSanitationHygieneHandwashing/hand hygieneSoapAlcohol-based hand rub/sanitizerCoughRubbish/wasteWaste disposal/segregationImmunization/vaccinationToilet/latrineAntimicrobial useDrugs/medicines/antibioticsTreatmentAppropriate use of medicines/prescription/prescribeAntimicrobialDrug resistanceDose/dosageAdherence/adhereAdminister/giveDrugs storageDrug manufactureDrug records/information/record keepingIn 2006, the Government of Uganda started implementing a strategic policy mandating science education for all learners in primary and lower secondary levels of education . ScienceUganda progressively built capacities for AMR containment over the last few years –42. UganWHO developed a curriculum framework for health workers to guide AMR curriculum inclusion for appropriate knowledge, skills, and attitude . HoweverAMR clearly illustrates the interdependence of human, animal, and environmental health, with the drivers and impacts of AMR experienced across all three sectors. The Government of Uganda made some efforts to implement multi-sectoral approaches for combating AMR, including by adopting the One Health approach and implementing the NAP-AMR. The policy brief presents a policy option with an additional opportunity for the line ministries working in One Health and MOES to draw lessons from NAP-AMR implementation and expand the scope of AMR content in school curricula to introduce the One Health concept at the primary level and continuously expand on those lessons in the secondary levels.Implementing this policy has potential implications for the AMR response. Introduction of AMR content early in the education system could strengthen the One Health response at all levels through creating knowledge about the AMR burden as a One Health challenge. The increased public awareness will ensure that the public takes more responsibility in the use of antibiotics and prevention of infections. Secondly, farmers, who constitute over 80% of the national workforce will be better equipped on handling antibiotics and other One Health related issues, contributing to better implementation of One Health actions through the knowledge gained through their earlier years in school. There is potential impact on the future designing and implementation of AMR containment activities in Uganda, with the potential of this policy brief to inform the planned revision of the Uganda NAP scheduled for 2024.Incorporating AMR training into existing education curricula can be a low-cost and sustainable strategy for countries to address AMR. This should be done as early as possible, with specific objectives in mind, to educate not only future health care workers but also the public about AMR. One effective approach could be to integrate AMR content into the school education curricula and extracurricular activities starting with pre-primary and primary levels and then continuing in secondary and tertiary levels of education. Targeting to introduce young children to some very simple concepts about AMR and its containment early could be a strategically strong approach for Uganda due to the high rates of primary school completion in the country . The conInclusion of AMR education into various regular school activities provides a significant opportunity for early AMR training. For primary school students, AMR-related concepts can be introduced through plays and fun-filled activities—including educational bingo, music and singing, and outdoor learning activities—to facilitate knowledge acquisition and retention, cognitive performance, and healthy development , 47. AndTo further underscore the significance of AMR containment, it is crucial to incorporate questions related to AMR in national examinations and school progressive assessments. By including such questions, learners will be motivated to delve into the subject matter and enhance their understanding and knowledge of AMR. Examinations have been shown to facilitate knowledge acquisition, especially as a result of exam preparations, and to improve memory and modulate memory formation . This inTo promote behavior-change at the community level, undertaking community-based education initiatives aimed at empowering local communities to understand AMR and their role in the fight against it can be of great significance. These initiatives reach a relatively high number of people in the communities and have been demonstrated to have a wide impact on antimicrobial use at population level . The iniIt is recommended that sections dedicated to the theme of AMR be created in the national museum as well as school and public libraries. Such an AMR section would serve as an educational hub, offering a comprehensive showcase of information on AMR. Visitors would have the opportunity to explore the history of antimicrobials, understanding their development and impact on health care. The section would provide valuable insights into the progressive rise of AMR, illustrating the factors contributing to its emergence and spread and the challenges it poses to global health. Exhibits and literature would highlight the consequences of AMR, such as the case of chloroquine—a once-golden drug for treating malaria that became obsolete due to unacceptable levels of resistance, which in turn led to the revision of national policies on malaria . This viAMR is a pressing global health issue that demands urgent attention. The low public awareness about AMR is among several significant risks to AMR containment efforts. Curriculum review to introduce content on AMR through early learning at primary and secondary levels of school education presents an easy, low-cost, innovative, and sustainable opportunity to help raise widespread awareness on AMR. Since completion of a primary level education is nearly universal in Uganda and the completion rate for secondary schooling is over 70%, the country can make significant strides toward its goal of AMR containment through the integration of AMR contents in school curricula. This can be a major intervention in support of public awareness, training, and education, which is currently neither systematically addressed nor well-funded even though it is a key pillar of Uganda's NAP-AMR.RK and JPW conceived the idea. RK, JPW, and MPJ wrote the first draft. JPW, RK, MPJ, JM, HKas, MM, HKaj, DS, KL, and NK each contributed knowledge and ideas. JPW, RK, MPJ, DS, and NK contributed to critical revisions. JPW, MPJ, and NK finalized the paper. All authors reviewed and approved the final version.


While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource ( Myeloid neoplasias can show complex mutation patterns and molecular features. Here, the authors apply machine learning to classify risk groups of myeloid neoplasia which may correlate with differential response to treatment. Until now, morphological features, later enhanced by cytogenetic abnormalities, have dominated the pathology criteria for MDS diagnoses. These can be limited by inter-observer variability, restricted resolution, and lack of functional correspondence to molecular underpinnings4. Widely-used MDS prognostic classification schemes may be convergent, as they group cases with similar features yet different molecular origins; or divergent, as they assign cases with similar molecular lesions into different pathomorphological sub-entities5. Moreover, when considering molecular features, morphology-based classifications overemphasize specific parameters , which may represent essentially the stage of the disease, as opposed to molecular evolution. As a result, blast-defined MDS subtypes would contain a mixture of cases with various molecular derivations9. Classification schemes according to clinical features are more practical, but apart from the weight of cytogenetics and molecular mutations on prognosis10, clinical prognostication does not reflect the disease pathogenesis12.The myelodysplastic syndromes (MDS) are a collection of diseases encompassing a pathologically distinct, broad spectrum of myeloid disorders, some of which represent stages of the natural history of leukemia15. These factors, along with cytogenetics, constitute the underpinnings of MDS pathogenesis. Given their complexity, attempts to consolidate mutational patterns into broader disease sub-entities have been made, with conventional integrative approaches of classical, clinical, and pathomorphological features used as a gold standard in supervised analytic strategies, including the new molecular international prognostic scoring system (IPSS-M)10. Consequently, the patterns of molecular features have been analyzed to fit into morphologic groups, with limited success given the complexity of mutations and their interactions, particularly with respect to disease progression16. Therefore, updated strategies may be needed to deconvolute this molecular diversity and generate subdivisions of patients with MDS whose disease fits within molecular pattern similarities, better reflecting prognosis and which could then be targeted with specialized therapeutic approaches. Machine learning (ML) analytic methods, as demonstrated in acute myeloid leukemia (AML)17, provide new opportunities to integrate the molecular pathogenesis by identifying relevant patterns, which could serve as molecular sub-entities19.The advent of next generation sequencing (NGS) has led to the discovery of a multitude of mutations in various genes and recognition of the tremendous molecular diversity and clonal hierarchy within myeloid malignanciesHere, we took advantage of a large, well-annotated cohort of patients with MDS and secondary AML (sAML) to test the hypothesis that related molecular patterns can be analyzed in an unbiased/unsupervised fashion to characterize molecularly defined configurations of MDS/sAML. We used a similarity-based ML approach to cluster patients into molecular subgroups, further validated based on clinical features.Among the 3588 patients included in this cohort, 735 (20%) had sAML, 774 (22%) had higher-risk MDS (HR-MDS), and 2079 (58%) had lower-risk MDS (LR-MDS). Abnormal karyotype was found in 1548 cases (43%)  according to distinct genomic features Fig. . The numn = 2870) and test  sets for five-fold cross-validation to assess the fit of our model (See supplementary for details). Based on the highest silhouette value in each fold, the majority of the folds (3 out of 5) showed 14 clusters as optimal number of total clusters, similar to the full cohort, suggesting robust strategy for our approach  compared to other clusters. The highest median hemoglobin level (11 g/dL) was observed in patients assigned to MC6, whereas patients within MC1, MC8, MC11, and MC13 had lower values (median around 9 g/dL).The composition of the MCs was clinically distinct, reflecting differing morphological diagnoses and bone marrow (BM) blast counts Table . For insWhen we applied reverse analysis, the majority of the sAML cases populated MC2 28%), MC13 (18%), MC1 (11%), and MC14 (10%). HR-MDS cases were mainly classified in MC2 (19%), MC6 (15%), MC13 (14%), and MC9 (11%). Finally, LR-MDS clustered in MC2 (27%) and MC4 . DNMT3A mutations were present in 20 and 24% of MC4 and MC10, respectively. MC2, MC6, and MC8 demonstrated distinct genomic signatures: MC2 included cases with NK only (100%) and some DNMT3A (11%), JAK2 (11%), and RAS pathway (10%) mutations; MC6 cases had similar features to MC2 but were also enriched in SRSF2 (49%) and RAS mutations (23%); MC8 was characterized by the presence of del5q/-5 (100%), DNMT3A (17%), and TP53 (17%) mutations. MC3 included cases with TET2 (100%), ZRSR2 (23%), and ASXL1 (21%) mutations with delY (54%). MC14 included cases with delY (42%) but without TET2 mutations, distinct from MC3. In contrast, MC12 included cases with TET2 (100%), ASXL1 (100%), SRSF2 (48%), RUNX1 mutations (40%), and NK (100%) similar to MC9, which contained ASXL1 (100%) with SRSF2 (34%) and RUNX1 (31%) but lacked TET2 mutations. While both MC10 and MC12 seemed similar regarding features such as TET2 mutations and normal cytogenetics, there were differences based on the absence and/ or presence of certain somatic mutations and cytogenetic abnormalities. The frequency of SF3B1 mutations was higher in MC10, while MC12 was enriched with ASXL1 mutations . MC7 was characterized by other cytogenetic abnormalities, not including del5q/-5 compared to MC8. MC1 was characterized by trisomy 8 (100%), ASXL1 (35%), TET2 (31%), and RUNX1 (24%) mutations. MC11 included cases with del7q/-7 (100%) and RAS pathway mutations (28%). Finally, MC13 contained cases with complex karyotype (100%) and TP53 (44%) mutations. The resultant MC signatures are illustrated in Supplementary Table MC5 grouped cases with del20q/-20 76%) and 6% and U2risomy 8 0%, ASXL1N = 2863) treated with hypomethylating agents (HMAs) and/or allogeneic hematopoietic stem cell transplant. Even after accounting for the different treatments, the risk groups continued to show significant differences in OS using semi-parametric Cox-PH model, where nonparametric alternative random-forest survival model showed improved separation in untreated cohort compared to treated samples as well  occurred in patients assigned to MC9, MC10, and MC12 , of which 29 and 71% of the treated cases had complete remission (CR)/ marrow complete remission (mCR) and hematological improvement (HI), respectively. In contrast, response rates in patients assigned to MC1, MC13, MC3, and MC7 were 13%, 13%, 14%, and 15%, respectively;  across the identified MCs Fig. . As expely; Fig. , of whicThis ML method focused on clustering patients with molecular similarities. The blast percentage within MCs did not appear to affect survival after 25 months. For instance, although MC13 contained 38 and 33% of LR and HR-MDS patients, respectively, the prognosis was homogenously worse when compared to other MCs. Using Cox-Proportional Hazard model accounting for relevant clinical variables including age, gender, BM blast percent, HMAs treatment, and allogeneic HSCT, the assigned risk groups based on our clustering showed significant survival differences  as a gold standard. The recent IPSS-M10 mitigates some of these issues but it still remains most heavily dependent on clinical parameters which represent stage of the disease rather than molecular pathogenesis10. Indeed, the tremendous diversity and complexity of molecular lesions hamper the application of conventional bioanalytic methods.While MDS classification schemes evolved as useful clinical diagnostic or prognostic tools, diagnostic criteria according to genomic signatures reflective of molecular pathogenesis have not been established10 but rather illuminate the genetic sub-classification of MDS and related conditions in an operator-independent fashion according to molecular correlations and mutual functional proximity. Despite the exclusion of anamnestic clinical criteria, the resultant scheme yielded a reproducible and validated system of genetically related disease clusters reflective of the genomic pathogenesis and prognosis, irrespective of established standards. Notably, our molecular classification has enabled the recognition of cases with convergent molecular mechanisms, e.g., for the rational selection of suitable therapies. Moreover, the personalized risk stratification method is independent of disease duration and stage. It does not involve blast count, whose predictive weight dominates most of the older disease schemes and the recently proposed molecular prognostication model11.To overcome the limitations of these traditional approaches, our study applied modern ML strategies to objectively integrate molecular features able to decipher patient sub-cohorts with known and/or previously cryptic associations. This strategy was not meant to compete with or replace current well-established prognostication tools,25. Moreover, the analysis of the invariant cluster-defining molecular combinations points towards relationships or convergent pathways. For instance, even historically well-defined subgroups such as MDS with deletion 5q, were included along with sAML cases within MC8, indicating that heterogeneous morphological subtypes can be grouped within the same cluster based on shared molecular lesions explaining common pathogenesis and disease behavior. Illustrative examples of such molecular associations are presented in the Our ML-based molecular model defines unique clusters according to the previously described genomic features and their combinations known to influence MDS and sAML outcomesTP53 and SF3B1 mutations, the recent IPSS-M assigns fixed scores for patients sharing certain mutations. However, as we illustrated in our MCs, not all patients with similar mutations will have similar pathophysiology and co-existing mutations. We demonstrated that patients with splicing factor mutations are assigned to different subgroups based on the presence of other epigenetic modulator mutations. Similarly, our results confirmed the functional distinction between monoallelic TP53 (mainly in MC8) and biallelic TP53 mutations (MC13). Recent studies have suggested a functional association between U2AF1 mutations and RNA-splicing genes located on chromosome 20 , this link was illustrated in MC5 in which U2AF1 mutations is associated with del20q26.With the exception of 5, our MCs were heterogeneous in this regard. This observation raises many questions about the implication of BM blast percentages on molecularly-based diagnoses. Indeed, our ML-based scheme indicates that BM blast may correlate more with the stage of the disease rather than the molecular architecture. For instance, although MC13 included patients with the worst prognosis, almost 1/3 of the cases in this cluster had low blast counts while sharing a similar molecular makeup with sAML, reflecting different stages of the same disease. Analogous observations were made in other clusters containing molecularly similar patients at various points of their clinical course. Significant survival associations with BM blasts and MCs also suggest that these variables capture different information regarding the disease pathogenesis. It is important to emphasize that the recent attempts to integrate cyto-molecular features into MDS classification for personalized approaches were also based on traditional clinical parameters, which always outweighed the variables derived from the genomic makeup. For instance, when analyzing the fraction of explained variation attributable to different prognostic factors for OS, BM blast percentage, age, and sex alone accounted for more than 50%. In comparison, molecular features only had limited power in the proposed model (~30%)11.Unlike previous prognostication systems highly dependent on the blast count10, the effects of differential blast counts are less eminent in patients already characterized by high-risk features 16. Using our molecular approach, a heterogeneous number of blasts was observed within individual MCs, indicating that cases with similar molecular make-up can present at different stages of the disease in which patients with higher blast percent represent a more advanced disease phase. Irrespective of the prognostic value of blasts, our study, because of its blast-agnostic approach, provides the advantage of identification of high-risk cases early in the disease course to implement the most appropriate therapies. Moreover, the distribution of prognostic groups (IPSS-R and IPSS-M) within MC , and some excluded molecular mutations, although rare, may have important clinical and pathogenesis impacts. Another limitation of any analytic strategy (supervised/unsupervised) is that less common mutations remain unappreciated because of the lack of statistical power. This is also a flaw of our approach, which we attempted to mitigate by combining mutations affecting the same functional pathways and identifying rare hits confined strictly to one cluster to allow for inferences in terms of their functional associations.In conclusion, despite the complexity and the diversity of molecular alterations in MDS and sAML, by deploying artificial intelligence analytics, we were able to discern functional and pathologically related, objective clusters irrespective of the anamnestic clinico-morphological features. The purpose of such classification is to identify patients with common features possibly suggesting a similar rate of sensitivity to targeted agents, and for the clinical investigators to rationally test efficacy of drugs in these molecularly-related sub-entities. The scoring systems benchmarked solely on survival may bundle patients with unrelated pathophysiology into the same groups, which if used for treatment indications (low risk disease vs. high risk disease) may result in heterogeneous response patterns. Molecular clustering would help to avoid such a scenario. Our model is available as an open-access resource for clinicians and researchers to establish relationships between molecular profiles irrespective of the stage of disease for rational selection of therapies.n = 1627), The Munich Leukemia Laboratory , and publicly available data sets 27 were combined to form a cohort of 3588 MDS and sAML patients  including chronic myelomonocytic leukemia (CMML), and sAML to generate a comprehensive genomic data set. Patient data from the Cleveland Clinic  32. The resultant model was validated internally and externally on an independent cohort (detailed description in the https://drmz.shinyapps.io/mds_latent).Our ML strategy was based on a consensus-clustering approach via autoencoders coupled with Gaussian-mixture modeling (GMM)Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


Despite some fluctuations in the prevalence in the 1980s, the support for visual materials, learning disabilities, robotics, self-monitoring tools, and word problem-solving instruction topics showed increasing trends, particularly after 1990. Some research topics, including apps and auditory support, have gradually increased in topic proportions since 1980. Topics including fraction instruction, visual-based technology, and instructional sequence have shown increasing prevalence since 2010; this increase was statistically significant for the instructional sequence topic over the past decade.The purpose of this study was to conduct a content analysis of research on technology use for teaching mathematics to students with disabilities. We applied word networks and structural topic modeling of 488 studies published from 1980 to 2021. Results showed that the words “computer” and “computer-assisted instruction” had the highest degree of centrality in the 1980s and 1990s, and “learning disability” was another central word in the 2000s and 2010s. The associated word probability for 15 topics also represented technology use within different instructional practices, tools, and students with either high- or low-incidence disabilities. A piecewise linear regression with knots in 1990, 2000, and 2010 demonstrated decreasing trends for the topics of  Worldwide, the use of technology has rapidly innovated over the last several decades. With the advances in technological tools and systems in society, information has become easily accessible. It is clear that students are now immersed in technology from childhood and exposed to a vast number of technologies. Living in the digital age, in which students and teachers use a range of digital devices in the classroom, technology-mediated education methods have penetrated deeply into the field of education  education. According to the National Center for Education Statistics , 7.2 milStarting in the late 1990s, educators implemented virtual manipulatives and technology-based three-dimensional interactive visual models for teaching mathematical concepts and skills  indexed in the Web of Science and published from 1970 to 2011. Applying content analysis, the authors found that more studies were published from 2006 to 2011 (44.7%) than during any other period; they identified the level of inclusion through an analysis of educational context , addressing participant characteristics and research design. They further classified ICT interventions into technical interventions in the pedagogical or wider context .Over the past few decades, researchers have been implementing content analysis to deepen the understanding of research topics and trends in educational technology, including for students with disabilities. Specifically, Istenic Starcic and Bagon  reviewedAdamu and Soykan  also appRecently, beyond reviewing the literature using the traditional approach of content analysis, researchers have applied text mining techniques to further consider nested data structures (tokens nested within words that were nested within a document) when reviewing unstructured text such as large bibliometric datasets. The text mining method enables researchers to examine how the words within each publication are associated, constructing the meaning for each topic. In an effort to display related text information, word networks have been used in the social sciences and been connected with bibliometric reviews  of several decades of research, semantic machine learning techniques can be beneficial in mitigating human subjective bias and reducing potential measurement errors , including systematic reviews and meta-analyses. Third, studies were journal articles or dissertations published in English between 1980 and 2021. Unpublished doctoral dissertations were included in the current review to cover the gray literature, reducing publication bias Paez, . When ann = 3548), Web of Science (n = 1677), Academic Search Complete (n = 1881), Education Source (n = 1657), APA PsycINFO (n = 1515), and MEDLINE (n = 604) for journal articles and dissertations published in English between 1980 and 2021, resulting in a total of 10,882 studies ; (b) not journal articles or dissertations (n = 1603); (c) not focusing on teaching mathematics for students with disabilities in K-12 grades (n = 3169), or not using technology in mathematics instruction (n = 2393). As a result, 488 studies were included in the in-depth text mining.We implemented the following procedure to extract bibliographic data of 7685 studies from EndNote  constructing a corpus by selecting a text column in the dataset (abstract) and combining texts with document-level variables (publication year); (2) constructing a token object by segmenting complex text into smaller words; and (3) constructing a document–feature matrix that displays the frequencies of features  for each document. As an initial step, the research team identified and manually extended 146 acronyms . The team applied tokenization by changing texts to lowercase; converting accented characters to the American Standard Code for Information Interchange; splitting hyphens and tags; and removing punctuation marks, symbols, numbers, and separators.quanteda R package  of each word. The idf of a term is a metric that shows the degree of distinction of words within documents, which is commonly defined using the following formula: idfi = dfi is the number of documents in the corpus containing word I, and N is the total number of documents in the corpus  out of the 4627 unique words and went down the list until we identified distinct words. In this process, 107 of 224 words were eventually included, creating the remaining 117 as the customized stop words list. The lists of the extended acronyms, customized dictionaries, and stop word lists are shared on the online repository  of words or patterns, that is, stop words, that were not distinct across documents. The team processed a predefined stop word lexicon using the English Snowball with a 175 stop words list Porter, , which iwidyr R package  across networks with different numbers of edges and nodes, we normalized the values to be between zero and one, with one being the central node where all nodes are connected. For the normalized degree centrality calculation, we followed the formula embedded in the igraph  = N is the number of nodes in the network corpus, and dv is the degree of node v. The visualization of the word co-occurrence network was completed through the tidygraph . Then, applying the pairwise_counts function in the d, dkθ denotes the probability that a topic is associated with each publication, and each observed word from a document , where n ∈ {1, …, Nd}, denotes that the nth word within a document has a corresponding topic assignment, randomly selected from a multinomial distribution given document-specific distribution over topics, zd,n ~ Multinomial (θd); a word is randomly selected from the corresponding multinomial distribution over terms conditional on the chosen topic, wd,n ~ Multinomial   + β2(year ≥ 1990)(year – 1990) + β3(year ≥ 2000)(year – 2000) + β4(year ≥ 2010)(year – 2010), where β0 = level (mean topic proportion) in 1980, centered at the year of 1980, β1 = trend (slope) in 1980s, β2 to β4 = trend change, and when year – knot = To achieve continuity of fit in the data over the entire publication period, we restricted the model by adding a dummy coding for the conditions of the three knots and constructed the following piecewise linear regression formula: stm R package  was passed to an ordinary least squares regression using the estimateEffect function in the The research team manually labeled the topics that emerged from structural topic modeling. The first author labeled each topic by reviewing the most frequently observed words and representative publications. Then, four other co-authors in special education technology, mathematics, and computer science reviewed each labeled topic with associated words and publications and shared the recommended labels for each topic. Agreement for labeled topics was 93.33% (the team agreed on 14 out of 15 topics), and the research team reviewed each label and discussed it until a consensus had been reached.median = 5). However, since 2010, there has been a rapid growth in the publication number. In the period 2010–2021, the minimum publication number per year was 11, and the maximum number was 46 (median = 27).A total of 488 studies were published in English between 1980 and 2021 in teaching mathematics using technology for students with disabilities in K-12 grades; 416 studies (85%) were journal articles, and 71 (15%) were dissertations. The number of publications has increased over the last 42 years. The number of publications by period was 37 for the 1980s, 51 for the 1990s, 70 for the 2000s, 243 for the 2010s, and 87 for years of 2020 and 2021. Between 1980 and 2009, the minimum publication number per year was 1, and the maximum number was 12 per year  in 37 studies. The word pairs with the highest co-occurrence (n = 9) were “learning disabled” with “program”, “computer” with “learning disabled”, and “computer” with “drill and practice”. A word with the highest level of degree centrality was “computer” , followed by “program”, “drill and practice”, “learning disabled”, “measure”, “level”, “testing”, “achievement”, “microcomputer”, and “computer-assisted instruction (CAI)”.In 1980 and 1989 see Fig. , a totalMin = 1, Max = 8, Median = 1) in 51 studies was greater than or equal to 4 (43 nodes and 174 edges). The word pairs with the highest co-occurrence (n = 8) included two sets: “strategy” with “problem solving” and “word problem” with “problem solving”. The next frequently co-occurring word pairs included “computer” with “program”, “learning disability” with “program”, “computer” with “CAI”, “learning disability” with “CAI”, “learning disability” with “computer”, “learning disability” with “problem solving”, and “word problem” with “strategy” (n = 7). The word with the highest degree of centrality was “CAI” . The following words with relatively high degree centrality included “computer”, “strategy”, “learning disability”, “problem solving”, “word problem”, “program”, “procedure”, “addition”, and “specific”.In 1990 and 1999 see Fig. , the co-Min = 1, Max = 10, Median = 1) in 70 studies was greater than or equal to 4 (52 nodes and 216 edges). The word pair with the highest co-occurrence included “learning disability” with “testing” (n = 10), followed by “learning disability” with “computer” (n = 9), “level” with “testing” (n = 8), and “learning disability” with “problem solving” (n = 7). The word with the highest degree of centrality was “learning disability” , followed by “testing”, “computer”, “level”, “problem solving”, “addition”, “achievement”, “measure”, and “CAI”.In 2000 and 2009 see Fig. , the co-Min = 1, Max = 27, Median = 1) in 330 studies was greater than or equal to 14 (51 nodes and 206 edges). The word pair with the highest co-occurrence was “functional relation” with “multiple probe” (n = 27), followed by “mathematical” with “practice”, “word problem” with “problem solving”, “MLD” with “learning disability”, and “mathematical” with “solving” (n = 26). The word with the highest degree of centrality was “learning disability” , followed by “mathematical”, “solving”, “practice”, “multiple probe”, “functional relation”, “addition”, “testing”, “intellectual disability”, and “problem solving”.In 2010 and 2021 see Fig. , the co-Figure CAI (θ = .111), instructional sequence (mean θ = .096), visual-based technology (mean θ = .093), learning disabilities (mean θ = .093), and word problem-solving instruction (mean θ = .077). The other 10 topics investigated in the field included calculators (7.0%), mathematics achievement (6.3%), testing (5.7%), robotics (5.4%), auditory support (5.1%), software (5.0%), support for visual materials (4.8%), apps (4.7%), self-monitoring tools (4.5%), and fraction instruction (4.4%). These topics could be categorized further into instructional practices and assessment, educational technology tools, and disability types. To supplement the characteristics of each topic, we specified the representative study for each topic, that is, the study with the highest topic proportion for each research topic.Table CAI, instructional sequence, word problem-solving instruction, fraction instruction, mathematics achievement, and testing) covered instructional practices and assessment. The CAI research topic has been the most frequently investigated over the past four decades. For example, as a representative study with the highest topic proportion, Nwaizu  covered the implementation of various educational technology tools for improving students’ mathematical performance. For the visual-based technology topic, representative studies with high topic proportions focused on technologies such as augmented reality, virtual reality, and virtual manipulatives. Miundy et al.  was specifically related to disability type. Namely, the learning disabilities topic emerged as a separate research topic across the corpus. This topic covered words such as “problem solving”, “game”, “mathematics learning disability”, “strategy”, “problem”, and “digital”. These highly associated words can also be corroborated by the representative study conducted by Huscroft-D’Angelo et al.  that were statistically significantly different from 0. Although not statistically significant, in 1980, the average topic proportion of the mathematics achievement topic  was also relatively higher than the remaining topics. In the 1980s and 2000s, the trends of topic proportion for the software topic were reduced by .017 and .018, respectively, compared with those of each previous decade (ps > .05). Although there was a slight annual increase in the topic proportion (a .017 increase) in the 1980s, the slope for the CAI topic showed a decreasing trend since 1990; in particular, compared with the 1980s, the trend was decreased by .048 in the 1990s, which was statistically significant (p < .05). Furthermore, even though there was a statistically significant increase in the trend for the mathematics achievement topic in the 2000s compared with that of the 1990s by .024 (p < .05), after 2010, this again reduced by .012 (p = .08). There was also a slightly increasing topic proportion trend in the 2000s compared with the previous decade of .012 (p = .29) for the calculator topic; however, the topic proportions in other periods were largely decreasing trends. Regarding the testing topic, the topic proportion was estimated to increase annually by .007 in the 1980s (p = .47); yet in the following decades, the topic proportions followed generally decreasing trends.Although there were fluctuations in the slopes, there were decreasing trends for apps, auditory support, fraction instruction, visual-based technology, and instructional sequence have shown increasing trends since 1980. Table apps and auditory support topics, topic proportions increased by .005 and .009 (ps > 0.05), respectively, in the 1980s. Starting in 1990, there was no noticeable change for these topics; comparable topic proportions were maintained throughout the remaining decades for the apps and auditory support topics. Furthermore, the topic trends of fraction instruction, visual-based technology, and instructional sequence showed similar patterns. Despite a slight fluctuation before 2010 for the instructional sequence topic (trend increase by 0.009 in the 1990s and decrease by 0.014 in the 2000s compared with each previous decade), there was a statistically significant increase in the topic trend after 2010 by .024 (p < .01); thus, the average topic proportion was expected to increase by .018 during the years between 2010 and 2021. Both the topics of fraction instruction and visual-based technology showed limited topic proportions until the year 2010; there was .002 or less annual increase in the topic proportions. After 2010, there were relatively large trend changes: by .010 for visual-based technology and .007 for fraction instruction. Although these topic evolutions were not statistically significant, even after 2010, the increasing trends of these last topics demonstrated the latest technology being used in teaching mathematics to students with disabilities.Although there were almost no existing publications in 1980, support for visual materials, learning disabilities, robotics, self-monitoring tools, and word problem-solving instruction topics showed increasing trends, particularly after 1990. Although statistically not significant, the average topic proportions of these topics increased after 1990. The topic proportion trends increased by .018 for the robotics and .013 for the learning disabilities topics in the 1990s, yet these were slightly reduced again in the 2000s (ps > .05). Regarding the topics of support for visual materials and self-monitoring tools, these topics also showed slightly increasing trends in the 1990s by .009 and .012, respectively, compared with those in the 1980s, and these increasing patterns persisted throughout the 2000s. Then, the trends for these topics declined after 2010. In the case of the word problem-solving instruction topic, after the initial decreasing trend in the 1980s, the topic trends increased slightly throughout the following decades; the trend change in the 1990s (.002) was relatively higher than in the other periods.The topic proportions for the We conducted a content analysis via word networks and structural topic modeling exercise targeting studies on teaching mathematics using technology for students with disabilities. We examined 488 journal articles or dissertations published from 1980 to 2021. Our purpose was to identify co-occurring words and research topics investigated over the past 42 years. Using document–topic and topic–word distributions, we investigated the highly associated words for each topic and the topic evolution over time. By aggregating the results at the journal or dissertation level, we found that publications covered topics of different degrees both during a specific period and over time.Applying word networks, we sought to identify co-occurring words for journal articles and dissertations on teaching mathematics using technology for students with disabilities and to examine the empirical evidence for centrality among the co-occurring words. The central word of “computer” in the 1980s and its co-occurring words such as “CAI”, “microcomputer”, and “software” demonstrated the features of the computer in this earlier time in the education field. The word networks in this period validated the emergence and early efforts in the development of microcomputers and computer software focusing on number facts, such as “multiplication facts” and “addition”, for students with learning disabilities , as well as applying videos, multimedia  for these topics represented studies related to the support of students with low-incidence disabilities, such as visual impairment, blindness, and hearing impairment  and topical prevalence. However, it is possible that topic-level covariates, such as types of studies that have been conducted , types of instructional practices , and educational technology tools , which cover the categories of each topic label, could be of higher level affecting the topical prevalence across the corpus. Therefore, in future research, a structural topic model that considers covariates of topic and document levels should be investigated.Three primary pedagogical and research implications were obtained from the current findings. The co-occurring word pairs that appeared within publications in each decade between 1980 and 2021 represented the usage and pattern changes of words in each time period. Although the use of computers through CAI was the most frequently researched topic in the 1980s and 1990s, especially for students with learning disabilities, we observed an exponential increase in research on mobile-friendly and online learning. Although disability types other than learning disabilities did not emerge as a distinctive research topic, the associated words probably supported the use of visual-based technology and videos for teaching word problems and basic mathematics to students with intellectual disabilities and autism spectrum disorder. These results indicate the need for research on students with other types of disabilities, particularly low-incidence disabilities. Future researchers can utilize educational technology applications, such as self-monitoring tools, robotics, auditory support, and support for visual materials, in their mathematics instruction for students with disabilities and validate the efficacy and roles of the interventions.The results indicate that educators, researchers, and policymakers could use text-mining techniques to identify the essential features of instructional practices and tools. The associated words for each topic can be considered key components in designing technology-mediated mathematics curricula for students with disabilities. However, these data cannot suggest how effective each of the technologies is in improving the performance of students with disabilities in mathematics. Thus, in future studies, systematic reviews and meta-analyses are needed to synthesize the findings across studies, analyze deeper engagement with the issues unearthed in each study, and investigate how the inclusion of each component differs according to the student variables  and the field of study variables . The public datasets and codes found in the current study will help future researchers replicate the suggested methodology, encouraging open and continuous communications in broadening the pathways for underrepresented groups of students in STEM and across disciplines.Finally, the current study indicates research trends that align with the development of educational technology within the infrastructure of the industry. Specifically, accessible technology tools  are more frequently used in studies that target only students with visual or hearing impairments (Adams & Cook, Observing the discussed research topics and their evolution over the past 42 years can provide educators with a comprehensive understanding of the changes in research focus regarding the use of technology in teaching mathematics to students with disabilities. Because of the explosion of new sources and publications, a traditional content analysis through the manual coding of each document might appear to be consuming excessive time and effort. Text mining approaches, such as word networks and topic modeling, could prove effective in extracting meaningful categories and themes from large datasets, such as biblical datasets. By applying the suggested methods, researchers and policymakers can efficiently understand the key patterns addressed in published documents.