Predictive power is shown to hold when the ML models are applied to an unseen set of compounds from the toxicology literature. Mechanistic insight is also achieved via the generation of full SQM transition state (TS) structures which are found to be very good approximations for the DFT-level geometries, revealing important steric interactions in some TSs. This combination of speed, accuracy, and mechanistic insight is unprecedented; current ML barrier models compromise on at least one of these important criteria.Modern QM modelling methods, such as DFT, have provided detailed mechanistic insights into countless reactions. However, their computational cost inhibits their ability to rapidly screen large numbers of substrates and catalysts in reaction discovery. For a C–C bond forming nitro-Michael addition, we introduce a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach that allows the prediction of DFT-quality reaction barriers in minutes, even on a standard laptop using widely available modelling software. Mean absolute errors (MAEs) are obtained that are below the accepted chemical accuracy threshold of 1 kcal mol A synergistic approach that combines machine learning with semi-empirical methods enables the fast and accurate prediction of DFT-quality reaction barriers, with mechanistic insights available from semi-empirical transition state geometries. Typically, the most telling insights come from analysis of competing TS geometries, from which key steric and electronic effects can be identified, and their respective reaction barriers. This allows us to rationalise the mechanisms and selectivities of a huge range of chemical reactions.3–6 In turn, this enables the rational design of new reactions and catalysts,7–9 and reduces the need for experimental trial-and-error approaches.In the last thirty years, the ease-of-use, power, and accessibility of highly sophisticated computational chemistry techniques has increased substantially.10–12 the cost of these methods remains limited by the level of molecular modelling method used in geometry optimisation and energy calculations. Despite some reported shortcomings,13–15 for example when TSs contain ion pairs,16 density functional theory (DFT)17,18 is one of the most widely used quantum mechanical (QM) reaction modelling techniques and has been involved in the successful modelling of countless reactions.2,19 However, a trade-off between speed and accuracy must be made; typical DFT calculations take on the order of hours to days, but this can be further exacerbated by the exact combination of functional, basis set and solvation model used, the number of atoms and complexity of the system in question, and the necessity, in most cases, to optimise many distinct chemical species and multiple conformations of each to draw practical conclusions.Although numerous toolkits have been developed to automate the location and subsequent optimisation of TS structures,20 Accordingly, several force field and force field-cost methods have been developed that allow the approximation of various thermochemical properties.21–31 However, many do not provide geometric information, or produce less accurate pictures of the TS, for example by treating them as minima. Furthermore, force field methods are often parameterised on niche training domains with expensive DFT calculations or experimental data or require complex and lengthy parameterisation procedures to be effective, limiting their transferability and making their implementation more difficult.In terms of calculation time, molecular mechanics (MM) typically improves on DFT by around six orders of magnitude.32–35 Additionally, many are embedded in widely available software packages, such as Gaussian,36 GAMESS,37 Spartan,38 MOPAC,39 and ORCA,40 and thus are straightforward to implement. Like force fields, SQM methods are several orders of magnitude faster than DFT, on the scale of seconds to minutes per calculation.20 However, this comes at the expense of some chemical accuracy as parts of the more expensive QM calculations are replaced with empirical parameters tuned via experiment or full QM.41 Despite some reported disagreements with more accurate ab initio methods, for example in the assignment of Diels–Alder reactions as either step-wise or concerted,42 SQM methods have been shown to produce reliable geometries for TSs of many reactions, including nucleophilic substitutions, isomerisations, alkene epoxidations, metal-catalysed oxidations, and even some cycloadditions.43–45 However, they require expensive, high-accuracy DFT single point energy (SPE) corrections to give reliable barriers,43 and thus SQM data alone is not of a sufficient quality for accurate reaction modelling.In contrast, most semi-empirical quantum mechanical (SQM) methods are extensively parameterised, and thus widely applicable to many areas of chemistry.46,47 Once a relationship is identified, the target property can be predicted from the features (inputs) of the model. Previously, ML has been used for the prediction of reaction rates and barriers derived from both experiment48–53 and high-level reference calculations.54–63 However, many models have errors significantly above the accepted threshold for chemical accuracy of 1 kcal mol−1,64,65 and offer little or no mechanistic insight, for example using molecular fingerprints or graphical representations of molecules. A few examples do make use of TSs in their predictions but use DFT to generate them, making the prediction process very time-consuming.48,66 Therefore, no current ML barrier model offers the combination of fast and accurate predictions with mechanistic insight derived from TSs. In recent years, several studies have used ML to bridge the gap between SQM and high-level QM, allowing prediction of various ground state thermochemical properties.67–71 We believe this combined SQM/ML approach could be used to improve current standards in the prediction of reaction barriers. Thus, we proposed to learn the relationship between simple, interpretable, and readily available SQM-derived molecular and atomic features and target DFT barriers, and hence afford DFT-quality barriers with the calculation speed of SQM. By calculating TSs using SQM, rapid mechanistic insight would also be available.Machine learning (ML) is an increasingly prevalent tool in the field of chemistry, used to identify patterns in chemical datasets.72 finding important applications in asymmetric catalysis5,73–75 and several natural product syntheses.72,76–79 Among the many classes of these versatile reactions, the nitro-Michael addition is one of the most useful;80–84 insertion of the nitro group into the organic framework via Michael addition enables a variety of synthetically important stereoselective reactions,85 and the resulting nitro compounds can be the precursor for an assortment of highly useful chemical functionalities, including pyrrolidines, lactones, aminocarbonyls, and aminoalkanes.86Herein, we use a synergistic SQM/ML approach to predict DFT-quality free energy activation barriers for a diverse class of C–C bond forming nitro-Michael additions . Michael87 to vary four positions of a generic α,β-unsaturated carbonyl Michael acceptor (MA) core with common organic fragments across synthesis,79 toxicology,88,89 and covalent drug design  from the toxicology literature to be used for external validation .36 Additionally, each MA was optimised with UFF for a comparison of SQM with classical force field methods.96 To incorporate the effect of solvent, SPE corrections were performed with the same method as the optimisation and the integral equation formalism of the polarisable continuum model (IEFPCM)97 with toluene. Temperature (298.15 K) and concentration-corrected (1 mol l−1) quasiharmonic free energies were calculated with GoodVibes98 and used to calculate reaction barriers . Full computational details are provided in the ESI, section 1.†All structures were conformationally searched using Schrödinger's MacroModel (version 12.7)A variety of simple and interpretable molecular and atomic physical organic chemical features were extracted for each MA and TS at each level of theory , k-nearest neighbour regression (NNR), random forest regression (RFR), gradient boosting regression (GBR), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian process regression (GPR). Feature selection and hyperparameter tuning were employed using scikit-learn99 and mlxtend100 within the 80% train set to prevent overfitting of the feature subsets to the regression models and optimise the models parameters, respectively.101 5-Fold cross validation (CV) was performed within the train set to generate mean absolute errors (MAEs). To assess the individual model performances, external validation was performed using the unseen 20% test set to generate MAEs with standard errors. To further assess the generalisability of the models, MAE scores with standard errors, which are comparable across different sample sizes, were also calculated for the set of 37 unseen reactions from literature. However, among these 37 reactions, two (E5 and E7) contain alcohol groups within their R-groups that allow intra- and intermolecular hydrogen bonding to take place within their respective MA and TS geometries , compared to an MAE of 0.92 kcal mol−1 over the other 35 reactions. Thus, herein, the final literature set was defined as only the 35 structures other than E5 and E7. Full details of the ML process and analyses are provided in the ESI, sections 3 and 4.†The enumerated dataset was randomly split into an 80% train set (800 reactions) and 20% test set (200 reactions) and the former used to train each feature subset to predict the DFT free energy reaction barrier. Seven scikit-learns Fig. S3. As no sThe test MAEs and standard errors  for each model and feature subset are provided in −1 for all feature subsets and each regressor. However, the performances of the kernel-based models  are the most remarkable, with each producing MAEs below the accepted threshold for chemical accuracy of 1 kcal mol−1.64,65 Indeed, examples of SVR,102,103 KRR,67,104,105 and GPR48,106 are prevalent in chemistry. These algorithms employ the Kernel trick, mapping the original input features into a higher dimensional feature space in a computationally efficient way and thus allowing the generation of highly complex models at a relatively low cost. In contrast, NNR is a conceptually simple method, and so the complex relationship between the input features and targets cannot be captured as easily. For the remaining models, the data presented does not appear to be of the most suitable form to deliver optimal performance. For example, ridge regression is a linear algorithm that relies on the features having strong linear correlations with the target barriers, which is often not the case in chemical systems;46 indeed, only three features across all levels of theory  of the nucleophile carbon for AM1 and PM6) have linear correlations (Pearson's r) with the DFT barrier that are above 0.7, often quoted as the threshold for collinearity.107 Similarly, the decision-tree-based models (RFR and GBR) generally lend themselves to the prediction of discrete data, such as reaction selectivities, rather than continuous variables such as reaction barriers.62,66 However, despite their apparent unsuitability, each of these models still produces MAEs approaching chemical accuracy, demonstrating the overall success of our SQM to DFT ML approach.Impressive results are achieved by all models, with test MAEs quenched below 2 kcal mol67 Indeed, Δ-ML has previously been found to perform well for out-of-sample predictions.58 Therefore, the use of feature subsets without both MA and TS information, as well as the reaction barrier, is generally not recommended.Evaluation of the average performances of the SVR, KRR, and GPR models  reveals Comparing performances across the different levels of theory, the classical UFF method is found to perform similarly to the AM1, PM6, and DFT MA-only subsets for the train and test sets. However, with only MA information available, extension of UFF to the literature set is poor and mechanistic insight from TSs is not available. In addition to these same drawbacks, the DFT MA subset also suffers from the relatively high cost of the DFT calculations; on a 16-core node, the average DFT MA calculation took over an hour, compared to 5, 14, and 32 seconds with UFF, AM1, and PM6, respectively. Furthermore, DFT calculations scale poorly as the size of the chemical structures become larger. Thus, the use of either UFF or DFT MA features are not generally recommended.35 Accordingly, the initial MAE between the PM6 and DFT barriers  was found to be slightly better than between the AM1 and DFT barriers . Nevertheless, AM1 was found to perform marginally better after ML, particularly when making predictions on the literature dataset; overall, the combined AM1 MA and TS feature subset (AM1 All) performed the best across the train, test, and literature sets.Conceptually, AM1 and PM6 are very similar methods; both are based on the neglect of diatomic differential overlap (NDDO) formalism and share several fundamental approximations, although PM6 is more extensively parameterised and makes several improvements to the core–core potentials.via GPR using the combined AM1 MA and TS feature subset with 101 features (GPR (AM1 All)), yielding train, test, and literature MAEs of 0.93, 0.96 ± 0.07, and 0.92 ± 0.18 kcal mol−1, respectively. Indeed, GPR was found to be the best model in several other studies for the prediction of thermochemical properties,48,106 however, using the same feature subset, both KRR(RBF) and KRR(poly) also produced models with all MAEs below 1 kcal mol−1. By plotting the AM1 and GPR (AM1 All)-predicted barriers against the DFT barriers, the improvement gained over the untrained model via our ML approach with respect to both the test set and literature set predictions can be visualised  model, the AM1 barrier was found to rank very highly in feature importance .109 Thus, the charge of the oxygen is a good indicator of the total electron density in the π-system of each MA. Accordingly, as the charge of the oxygen becomes more negative, interaction between the MA and negatively charged nucleophile becomes more difficult, and the reaction barrier increases. In addition, several other features encoding electrostatic information for specific atoms  are important, including orbital electronegativities (PEOE), dispersion descriptors (Pint), and electrotopological characteristics (EState). Overall, these features account for a significant proportion of the electrostatic component of the nitro-Michael addition reaction.The next most important feature, and the most important for the literature set, is the Mulliken charge of the carbonyl oxygen. The conjugated π-system of α,β-unsaturated carbonyls is soft and highly polarisable, whilst oxygen is a highly electronegative atom.89,110 For example, steric parameters in the vicinity of the α,β-unsaturated carbonyl describe the steric accessibility of the β-carbon, where the nucleophile needs to be for 1,4-Michael addition to occur, and thus correlate very strongly with the reaction barrier.Finally, several steric features, including sterimol parameters, PBVs, and solvent accessible surface areas (SASA), comprise a substantial proportion of the remaining high-importance features. This is in line with previous findings that a significant extent of steric control exists in the rate of Michael addition reactions with glutathione.via visualisation of the geometries, without the need to analyse individual feature importances. But how accurate are these SQM geometries, and can they be used as approximations for the DFT TS geometries?The top-performing features of GPR models with subsets derived from PM6 calculations, in addition to each of the SVR and KRR models with AM1 features, were of a similar nature to GPR (AM1 All) f theory . In the via SPE corrections results in highly flexible models that allow different solvents to be incorporated without reoptimising every structure. However, to examine the impact of solvent, AM1, PM6, and DFT TS optimisations with the IEFPCM solvent model were performed on the literature set. We then superimposed each pair of gas and solvent optimised TSs and calculated average RMSDs of 0.13 Å for AM1, 0.30 Å for PM6, and 0.16 Å for DFT , and accurate DFT-level barriers and geometries can be obtained. Although users may be concerned that the deficiencies of a particular baseline method, for example poor applicability to a particular chemical domain (see introduction), may inhibit a model's ability to make accurate predictions, our results demonstrate that feature subsets derived from both classical force field and SQM methods all lead to comparable predictive performances. Thus, the user may simply select an appropriate baseline method for the reaction in question and, by the same principles described above, accurate predictions should be possible. For a more detailed account of the applicability of SQM methods in modelling organic chemistry, we recommend a review by Thiel.113For the nitro-Michael addition reaction investigated here, the full DFT level of theory took approximately 7 hours on a 16-core node to obtain each of the 1000 enumerated MA and TS structures, compared to only 51 seconds for the respective AM1 calculations Table S3. On a 1-Overall, the combination of highly accurate SQM geometries and ML-derived energies represents a significantly cheaper way to obtain very good approximations of DFT-level geometries and energies. In turn, this enables the rapid prediction of reaction barriers and delivers mechanistic insight for this essential class of nitro-Michael additions, which could lead to much faster screening of these kinds of reactions, and thus much more efficient design of new synthetic methodology.−1 were achieved with a calculation time of seconds, even when making predictions on an unseen set of compounds from the toxicology literature. Evaluation of the most predictive features provided clear insights into important aspects of the nitro-Michael addition reaction mechanism. However, SQM geometries of TSs, and to some extent MAs, were found to be very good approximations to the full DFT TS geometries and thus offer mechanistic insight with no additional work required. Combination of these SQM geometries with highly accurate ML-derived energies allows the prediction of barriers and the screening of reactions at DFT level, without the need for time-consuming DFT calculations. No current ML barrier models offer our combination of speed, accuracy, and mechanistic insight. The generalised nature of the study means the ML approach can be highly customised, for example by choosing from various regression algorithms, features, and molecular modelling methods. We believe that the same principles could also be applied to achieve the rapid prediction of reaction barriers and mechanisms for other important classes of chemical reactions, paving the way for more efficient drug discovery and rational reaction design.We have combined ML and SQM calculations to achieve the fast and accurate prediction of DFT-quality free energy activation barriers using widely available computational techniques. Using a variety of regression algorithms with simple and highly interpretable features, MAEs below the accepted chemical accuracy threshold of 1 kcal molhttps://doi.org/10.15125/BATH-01092.Gaussian16 output files for all computed structures are openly available in Dataset for "Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction" in the University of Bath Research Data Archive at This manuscript was written through contributions from all authors.There are no conflicts to declare.SC-013-D2SC02925A-s001


Our purpose was to investigate the influence of atlas library size and CT cross-slice number on the accuracy and efficiency of the atlas-based auto-segmentation (ABAS) method for the automatic contouring of clinical treatment volume (CTV) and organs at risk (OARs) during cervical cancer radiotherapy.Of 140 cervical cancer patients, contours from 20, 40, 60, 80, 100, and 120 patients were selected incrementally to create six atlas library groups in ABAS. Another 20 tested patients were automatically contoured with the ABAS method and manually contoured by the same professional oncologist. Contours included CTV, bladder, rectum, femoral head-L, femoral head-R, and spinal cord. The CT cross-slice numbers of the 20 tested patients included 61, 65, 72, 75, 81, and 84. The index of dice similarity coefficients (DSCs) and Hausdorff distance (HD) were used to assess the consistency between ABAS automatic contouring and manual contouring. The randomized block analysis of variance and paired t-test were used for statistical analysis.post-hoc least significant difference comparison indicated that no significant difference had been found between different atlas library sizes with DSC and HD values. For ABAS efficiency, the atlas library size had no effect on the time of ABAS automatic contouring. The time of automatic contouring increased slightly with the increase in CT cross-slice numbers, which were 99.9, 106.8, 114.0, 120.6, 127.9, and 134.8 s with CT cross-slices of 61, 65, 72, 75, 81, and 84, respectively.The mean DSC values of “CTV, bladder, femoral head, and spinal cord” were all larger than 0.8. The femoral head and spinal cord showed a high degree of agreement between ABAS automatic contouring and manual contouring, with a mean DC >0.80 and HD <1 cm in all atlas library groups. A A total of 20 atlas library sizes and a minimum CT cross-slice number including CTV and OARs are enough for ensuring the accuracy and efficiency of ABAS automatic contouring during cervical cancer radiotherapy. Radiotherapy, as an independent treatment strategy, plays an important role in cervical cancer treatment . DelineaThe atlas library should be set before applying the ABAS method for delineating contours automatically. According to published results, multiple-ABAS could overcome the issues encountered with single-ABAS, such as large discrepancies in volume and location between the atlas library and subject data . HoweverThis retrospective study included 140 patients with newly diagnosed, pathologically confirmed stage II–III cervical cancer (7th edition of the AJCC staging system). All patients were treated with volume-modulated arc therapy (VMAT) technology in the Radiation Oncology Department at the First Affiliated Hospital of Xi’an Jiaotong University from October 2020 to October 2021. The VMAT plan was delivered at the prescribed dose of 50 Gy (25 fractions) to the cervical tumor. Neoadjuvant, concurrent, or adjuvant chemotherapy was recommended for patients.A total of 140 planning CT images from 140 patients were collected. All patients were immobilized using thermoplastic body mold in the supine position with hands raised and arms crossed with elbows on the top of the head during CT simulation. All patients were instructed that the rectum should be completely empty and the bladder should be filled with 300 ml of water 2 h before CT scanning. Each patient received a helical CT scanning under free breathing conditions on a 16-slice CT scanner . The scanning parameters were as follows: pixel spacing 1.1543 mm × 1.1543 mm, matrix 512 × 512, pitch 0.85, 120 kV, 400 mAs, thickness 3 mm, and layer spacing 3 mm. The scanned images were sent to a MIM Maestro version 6.7 software  with ABAS function based on a computer (Intel(R), Xeon(R), E5-1603 CPU, 2.8 GHz, four processors, 32 G RAM).The professional oncologist manually contoured CTV and OARs on the CT-scanned images of all patients with reference to RTOG 0418  and consOf 140 cervical cancer patients, 120 patients were randomly registered in six different atlas library groups with 20, 40, 60, 80, 100, and 120 patients incrementally for ABAS. The automatic segmentation process was performed in the 20 remaining tested patients with ABAS. The details are shown in Contours generated by ABAS automatic contours were compared with manually corrected ABAS automatic contours. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to evaluate the accuracy of automatic contouring . DSC wasRandom analysis of variance and paired-sample t-test were used to analyze the accuracy of automatic contouring results between the different atlas library sizes and CT cross-slice numbers. P < 0.05 was regarded as statistically significant. SPSS 22.0 was used for statistics analysis.A total of 20 cervical cancer patients’ CTV and OARs were delineated both by a radiation oncologist manually and ABAS automatically with an atlas library size of 20. The consistency between automatic and manual segmentation was assessed with DSC and HD values. According to the results, the mean DSC values of “CTV, bladder, femoral head, and spinal cord” were all larger than 0.8. However, the mean DSC value and HD value of the rectum were 0.695 and 2.508 cm, respectively. Therefore, the contours of the rectum needed to be corrected greatly before clinical application as shown in Post-hoc least significant difference (LSD) comparisons indicated that no significant difference was found between different atlas library groups as shown in ABAS was used to delineate the contours of “CTV and OARs” of cervical cancer patients under different atlas library groups . The DSC and HD values of CTV and OARs in the different atlas library groups are shown in One-way ANOVA was adopted to investigate the influence of atlas library size and CT cross-slice number on the efficiency of ABAS automatic contouring. The atlas library size had no effect on the time of ABAS automatic contouring, as shown in Our study investigated the influence of atlas library size and patients’ CT cross-slice number on the accuracy and efficiency of ABAS and established an optimal atlas library and CT cross-slice for automatic contouring during cervical cancer radiotherapy, which was rarely discussed in previous studies. ABAS was introduced to delineate CTV and OARs automatically and reduce interobserver and intra-observer contouring variability. Previous studies , 18 demoDSC was used to assess the overlap ratio between ABAS automatic contouring and manual contouring. In our study, we found that ABAS showed acceptable accuracy in delineating CTV and parts of OARs such as bladder, femoral head-L, femoral head-R, and spinal cord, with the mean DSC of 0.816, 0.866, 0.867, 0.873, and 0.858, which was larger than a good overlap standard of 0.7 according to a published paper . For ABAEarlier work by Stuart Greenham et al.  evaluateRecently, the scope of auto-segmentation has been expanded to arterial intelligence (AI)-based contouring using deep learning algorithms –29. EarlThere are several limitations in the current study. First, a cohort of patients was included in all the atlases used in six different atlas libraries. This pool could be overrepresented, and this issue could influence somehow the results of the work. Therefore, further investigations including a large number of independently tested patients are needed for evaluating the efficacy of the current ABAS library. Second, the research of this approach only evaluated the influence of CT cross-slice numbers on the time of ABAS automatic contouring. Future work will be required to evaluate the influence of CT cross-slice number on DSC and HD values of ABAS automatic contouring.In our study, 20 atlases and a minimum CT cross-slice number could insure the accuracy and efficiency of ABAS automatic contouring during cervical cancer radiotherapy. Considering that ABAS could not delineate the rectum organ accurately, manual correction by a radiation oncologist is necessary. The data that we used in this study was from only one department, which means that the model may not apply to data with different situations. A larger dataset and more data sources could make the result more robust and have a better generalization capability.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author/s.WW and XZ planned and designed the research. YL, YS, and DY performed the experiment and wrote the manuscript. WW, YKL, and YZ revised the manuscript. YKL, LW, and YW analyzed the data. All authors contributed to the article and approved the submitted version.This work was supported by the Key Research and Development Projects of Shaanxi Province, China (No. 2022SF-440).All authors in this study and many colleagues helped to collect and analyze data. Their support and helps should be appreciated.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Contrary to most European guidelines, benzodiazepine receptor agonists (BZRA) are often used continuously at a low dosage, being the most common form of long-term use. In Belgium, BZRA use is monitored by analyzing self-report data about medication use in the last 24 h. This method provides insufficient insight into the terms of use of these psychoactive drugs.To describe trends in BZRA prescribing in Flanders, Belgium, between 2000 and 2019.Population-based trend analysis and a case-control study for the year 2019 were done with data from a morbidity registry in general practice.Repeated cross-sectional and joinpoint regression analyses revealed trends in sex- and age-standardized prescription rates among adult patients (18+).Overall, BZRA prescriptions increased. The highest overall increase was found among male patients 18–44 years old, with an average annual percentage change of 2.5 . Among 65+ female patients, a decrease was found since 2006, with an annual percentage change of −0.7 . In 2019, 12% of registered patients received minimally one prescription, long-term use was observed in 5%, back pain was the most common morbidity significantly associated with a rise in BZRA prescriptions, and zolpidem was the most prescribed BZRA (22%).Despite some statistically significant decreasing trends, an overall increase in BZRA prescriptions was observed throughout the 19-year study period, especially among long-term users of 18–44 years and 65-plus. Zolpidem became the most prescribed BZRA and warrants more attention. In 2018, Belgium reported the highest consumption rate of zolpidem and the third highest consumption rate of benzodiazepines worldwide (BZRA are psychoactive drugs that enhance the effect of the neurotransmitter gamma-aminobutyric acid (GABA) in the central nervous system –9. SedatThe majority of BZRA in Belgium is used as hypnotics and anxiolytics, for which multiple expert groups promote a reticent policy. Belgian prescribing guidelines concur with the guidelines in most European countries, stating that BZRA should be used in the lowest possible dose and for the shortest possible duration, i.e., maximally 1–2 weeks for insomnia, and 2–4 weeks for anxiety , 30–33. Although long-term use may be medically justified for some patients , it is aIntego, which stands for “integrated computerized network”, was established by the Department of General Practice of KU Leuven in Belgium, in 1990. The database of this network contains demographic, clinical, biomedical, and prescription data, which are collected during general practitioners' daily practice. Registration with computer-generated keywords, in the electronic health record, provides a link to classifications such as the International Classification of Primary Care ICPC-2) for diagnoses, and WHO's Anatomical Therapeutic Chemical (ATC) for medications , 2. Part for diagThe ethical committee of KU Leuven Medical School and the Belgian Privacy Commission approved the Intego procedure .A population-based trend analysis was done with data collected from 2000 to 2019. Data from the years 2020 and 2021 were excluded to prevent potential bias by COVID-19. A case-control study, in which controls were patients who did not receive BZRA prescriptions, with data from 2019, was performed to contextualize population characteristics. Patients 18 years or older who received minimally one BZRA prescription were selected.BZRA were defined as the ATC classifications N03AE, N05BA, N05CD, and N05CF. Two groups of patients are compared: patients who received <3 BZRA prescriptions per year, and those who received three or more prescriptions in 1 year. This prescribing pattern corresponds with the most common definitions of long-term use in interventional trials, being 3–6 months of BZRA use , 41–53.A repeated cross-sectional analysis, using a Chi-square trend test with a confidence interval of 95%, was conducted for two time periods (2000 and 2019) to investigate changes in BZRA prescription rates, diagnoses, and prescriptions of other psychoactive medication. This method was also used in the case-control study. Per case, three optimally chosen controls were used. They were matched for practice, age—with a maximum difference of 2 years, and sex. These analyses were performed with R version 4.0.3 and the ccoptimalmatch R package , 55.*Stat package from the Surveillance Research Program of the US National Cancer Institute was used to perform JPRA , annual prescription rates were calculated in the total study population and in different groups: male and female, occasional and long-term users, 18–44 years, 45–64 years, and 65 years or older (65+). JPRA is a well-known method for identifying and studying statistically significant trends over time . The poiorm JPRA .n = 7,209) received minimally one BZRA prescription. By 2019, this increased to 12% , which corresponded with a 2% rise in the sample of patients with three or more prescriptions in 1 year, and a 1% rise in the sample of patients with <3 prescriptions in 1 year . Of all patients who received a BZRA prescription, over 60% were female. In 2019, the most common diagnoses in the study population were back pain, hypertension, depression and cancer. Insomnia and anxiety held the sixth and eighth positions. All co-morbidities except depression increased significantly since 2000. Finally, a significant rise of 15% in concomitant opioid prescriptions was observed between 2000 and 2019. Among patients with three or more prescriptions in 1 year, 45% had received a prescription for opioids in 2019. A similar but less pronounced rise was also found for antidepressants, from 38 to 46% .From 2000 to 2019, back pain had risen by an average factor of 1.35 in both samples . This diIn all age categories, a statistically significant increase was found as final trend, starting in 2012  and 2016 . In the category 18–44 years, also the overall prescription rate increased, with a significant AAPC of 1.4 . Analyzing the sex-standardized trends, an overall rising trend was found in male patients, with an AAPC of 0.9 . In female patients, a significant rising trend since 2012 was found in all age categories except 65 years or older. Detailed results are shown in Although not all statistically significant, the latest trends in this sample were decreasing trends with APCs ranging from −4.3  to −0.7 , except among female patients between 18 and 64 years. In the youngest category of female patients, an overall increase was found . In the category of 45–64 years, trends fluctuated more .In all categories, significant increases were found before any decreasing trends, resulting in overall rising trends with significant AAPCs ranging from 1.0  to 2.5  . The fluInappropriate BZRA prescribing, at odds with current guidelines, seems to be highly prevalent in Belgium. First, throughout the 19-year study period there was an increase in patients receiving three or more BZRA prescriptions in 1 year, particularly among male patients 18–44 years old and female patients 65 years or older, despite some significant decreasing trends. Second, back pain was the most common diagnosis associated with BZRA prescribing, even though Belgian guidelines recommend against the use of muscle relaxants . DiagnosIn comparison to registry-based data from 2014 to 2015, long-term BZRA use, approximated by receiving three or more BZRA prescriptions in 1 year, is one to two percent more prevalent in Belgium than in European countries such as France and Finland , 59. ConAlthough previous studies have shown that long-term BZRA use is most commonly related to various psychiatric conditions, such as anxiety and depression , 35, 63,Finally, in the youngest age category, 18–44 years, all significant findings are rising trends, with an overall increase  among patients who received three or more prescriptions in 1 year. Moreover, in 2018, Sidorchuck et al. reported that in Sweden 31% of all 18–24 years old BZRA users received prescriptions to use this medication for more than 6 months . These fTo our knowledge, this is the first registry-based research that covers a study period of 19 years to describe BZRA prescription trends in primary care. Moreover, comparing trends between two groups of patients, differentiating long-term use from other use, was discussed in only one other recent study . FurtherFinally, during analysis it was difficult to compare the observed prescribing rates with reports from other countries because of differences in the origin of the datasets, patient populations, definition of long-term use, and time periods. However, in their systematic review, Kurko et al. suggest defining long-term use as at least 6 months' use or longer during 1 year, for future research . On the To improve (de)prescribing practices, the BZRA situation in Belgium demands both interventional and epidemiological studies. First, implementation research to increase non-pharmacological treatment and discontinuation interventions is required. This could be linked to mapping the patients' access to mental health services and accessibility of care. Future interventions could also focus more on empowering patients to discuss their medication use and possible non-pharmacological treatment options. Moreover, policy and guidelines should motivate general practitioners to discontinue long-term BZRA use that is no longer medically justified. Tools that help them regularly review their prescribing practices could be useful in this matter. Second, the significant prevalence of back pain and concomitant opioid prescribing in a BZRA-consuming population warrants further research. Since both BZRA and opioids have sedating effects for which tolerance is rather quickly developed, the impact of concomitantly prescribing them in this population should be investigated. Third, the Belgian guidelines on the treatment of insomnia recommend a maximum of 1 week of pharmacological treatment, yet 22% of all BZRA prescriptions in the group that received three or more prescriptions per year are for zolpidem, a hypnotic drug. Professionals' attitudes toward z-drugs –62 may lThe original contributions presented in the study are included in the article/CM conceived the study. PM prepared the dataset and performed the cross-sectional and case-control analyses. KC performed the joinpoint regression analysis with guidance of PM. KC wrote the first draft of the article. All authors contributed to the study design and approved the final version of the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Gliomas, originating from the glial cells, are the most lethal type of primary tumors in the central nervous system. Standard treatments like surgery have not significantly improved the prognosis of glioblastoma patients. Recently, immune therapy has become a novel and effective option. As a conserved group of transcriptional regulators, the Sry-type HMG box (SOX) family has been proved to have a correlation with numerous diseases. Based on the large-scale machine learning, we found that the SOX family, with significant immune characteristics and genomic profiles, can be divided into two distinct clusters in gliomas, among which SOX10 was identified as an excellent immune regulator of macrophage in gliomas. The high expression of SOX10 is related to a shorter OS in LGG, HGG, and pan-cancer groups but benefited from the immunotherapy. It turned out in single-cell sequencing that SOX10 is high in neurons, M1 macrophages, and neural stem cells. Also, macrophages are found to be elevated in the SOX10 high-expression group. SOX10 has a positive correlation with macrophage cytokine production and negative regulation of macrophages’ chemotaxis and migration. In conclusion, our study demonstrates the outstanding cluster ability of the SOX family, indicating that SOX10 is an immune regulator of macrophage in gliomas, which can be an effective target for glioma immunotherapy. Gliomas, originating from the glial cells, are the most lethal type of primary tumors in the central nervous system (CNS) . AccordiRecent studies have found that as a constitutive part of the tumor microenvironment (TME), tumor cells, stromal cells, and infiltrating immune cells all serve a variety of biologically important roles in glioma proliferation, progression, and prognosis . Moreovevia paracrine signals, and vice versa (Sry-type HMG box (SOX) family proteins are a conserved group of transcriptional regulators depending on the high-mobility group (HMG) domain to bind with DNA . The SOXce versa . By givice versa , 18. Thehttp://singlecell.broadinstitute.org). Cluster analysis was performed, and SOX10 was identified as a distinguished biomarker to explore the prognostic value and association with the glioma immune microenvironment.Herein, our study extracted data from bulk tumor  and single-cell mRNA-seq databases  and CGGA (http://www.cgga.org.cn/). 672 samples in TCGA were used as the training set, while 1013 samples in CGGA were used as the validation set. We excluded samples with insufficient OS. The RNA-sequencing data, SCP50 and SCP393, was collected form Single Cell Portal platform (http://singlecell.broadinstitute.org). To possessing a similar signal intensity with the RMA- processed values, the fragments per kilobase million (FPKM) values were transformed into transcripts per kilobase million (TPM) values.1685 samples of diffuse glioma related data and complete clinicopathological annotations were obtained from two datasets: TCGA (https://cloud.genepattern.org) to assess the landscape of CNV, including the frequency of function mutation gain or loss at the amplified or deleted peaks.We obtained the somatic mutation and copy number variant (CNV) profiles from TCGA dataset. We used GISTIC 2.0 analysis  to ensure the clustering tendency. The LASSO-LR algorithm, Pamr algorithm, random forest algorithm, XGboost algorithm, and Boruta algorithm were used to screen out the most characteristic genes, SOX10.http://timer.cistrome.org/) web server was used to thoroughly evaluate the degree of immune infiltrating cells in gliomas. We used the xCell algorithm to ascertain the enrichment levels of 64 types of immune cells. The proportions of 22 types of TME cells in tumor tissues were evaluated by the CIBERSORT algorithm. The R gene set variation analysis (GSVA) package was implemented to calculate enrichment scores by single-sample gene set enrichment analysis (ssGSEA). Besides, the EPIC algorithm, MCPcounter algorithm, and QuantSeq algorithm were also executed to estimate the immune infiltrating cell abundance. Using ssGSEA, we evaluated the seven steps of cancer immune cascades. This immunity cycle determined the destination of tumor cells and reflected the immune response of the anticancer. The subMap algorithm was used to evaluate the response to therapies of anti-CTLA4 and anti-PD1. Also, the Tumor Immune Dysfunction and Exclusion (TIDE) (http://tide.dfci.harvard.edu/setquery/) and Tumor Immune Syngeneic MOuse (TISMO) (http://tismo.cistrome.org) algorithm was utilized for deducing the immune checkpoint blockade immunotherapy responses in gliomas.The Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm was used to estimate the stromal score, immune score, and estimate score of the infiltrating immune cells in the TME. The Tumor Immune Estimation Resource 2.0 , with ethics approvement. SOX10 , CD163 , and CD68  were the primary Abs. Horseradish peroxidase-conjugated secondary antibody incubation  was the secondary antibody. The tyramide signal was amplified into TSA . The stained slides were scanned using the TissueFAXS platform . The spatial analysis of the stained cells was performed using the StrataQuest software .https://www.cancerrxgene.org/). The semi-inhibition rate (IC50) reckoned by the pRRophetic R package was utilized to predict the drug susceptibilities and responses.All pharmacogenomic data were downloaded from the Genomics of Drug Sensitivity in Cancer  with the log-rank test. All OS curves were produced by the survminer R package. Mutation landscape OncoPrint was executed by the maftools R package. Heatmaps were pictured found on the R package complexHeatmap. Student’s t-test was conducted to analysis normally distributed variables between the two groups while one-way analysis of variance (ANOVA) was conducted to analysis normally distributed variables between multiple groups. The Wilcoxon test was applied to analysis non-normally distributed variables between the two groups while Kruskal-Wallis test was applied to analysis normally distributed variables between multiple groups. R 3.6.3 was used to conduct all statistical analyses. Statistics were considered significant when p-value< 0.05.. To choose the ideal cluster number, the stability of clustering was appraised by the ConsensusClusterPlus package in TCGA . It was found that k = 2, with the flattest CDF curve, is the optimal choice . Then, clustering tendency was evaluated by principal component analysis (PCA). We used blue dots to represent cluster1, while red dots represent cluster2. SOX clusters were separated significantly, indicating a high-quality consensus cluster result .Firstly, we evaluated the clustering capabilities of the SOX family and visualized it . To choo. To choo. To choo. Besides, We further explored the overall survival of glioma patients in cluster1 and cluster2, p < 0.001. The Kaplan–Meier curves firmly demonstrated that cluster2 had higher and more prolonged survival than cluster1 . Besides. Among these three evaluations, scores of cluster1 were all higher than those of cluster2. Moreover, they could be thought to have a significant difference, on account of p < 0.001. Then, we calculated the proportion of five immune subtypes in the two clusters . Immunologically Quiet was generally presented in cluster2 (more than 50%) and was partially observed in cluster1 (less than 50%). On the contrary, Lymphocyte Depleted was the frequentist immune subtype in cluster1, which took up over 50%. Moreover, we found that the TME indicator scores of cluster2 seemed to be lower than those of cluster1, which indicated a weaker immune response . It also revealed the difference between the two clusters.We investigated the TME characteristics of cluster1 and cluster2. We evaluated the ESTIMATEScore, ImmuneScore, and StromalScore of the two clusters in TCGA dataset . Among t. Among t. We defined four subtypes of glioma: pro-neural (PN), classical (CL), neural (NE), and mesenchymal (ME), among which CL and ME are more severe , single nucleotide variant (SNV), deletion, insertion, and intergenic region (IGR). The frequency of insertion and deletion seemed to be non-statistically different, while SNPs were a little more common in cluster1 . Amplifi. We used a Venn diagram to take the intersection of the five algorithms . These two characteristic genes in the intersection corner, SOX10 and sex determining region Y (SRY), were the most potential to best classify the two clusters  curves in TCGA dataset . The results firmly proved that SOX10 was a predictor of IDH and subtype. The ROC curves exhibited high sensitivity and specificity, with all areas under the curves (AUC) bigger than 0.7 and 0.9.We performed a survival analysis of different SOX10 expressions in pan-glioma, LGG, and GBM based on TCGA and CGGA datasets . The KapAdditionally, to probe the latent pathological function of SOX10, the KEGG and GO enrichment analyses were performed. , SOX10 was negatively correlated with T-cell dysfunction, implying its potential impact on immunotherapy. Specifically, SOX10 positively correlated with the normalized Z score from selection log2FC in the CRISPR screen dataset and normalized expression value from immune-suppressive cell types.As manifested in the heatmap Metabolism has been considered a vital determining factor in the survivability and potency of immune cells . We expl. We also explored the semi-inhibition rates of gefitinib and nilotinib. The results showed that the estimated IC50 was higher in low-expression SOX10 than in high-expression . Another noteworthy observation was that SOX10 could significantly predict immunotherapy response, whose responders were correlated with SOX10 levels . Besides, SOX10 could significantly predict the cytokine treatment of immune effector molecules in four immunotherapy cohorts . We also computed the biomarker pertinence of SOX10 by comparing it with normalized biomarkers based off of their prognosticative ability of response outcomes and OS of human immunotherapy cohorts. Fascinatingly, it was found that SOX10 gave an AUC of more than 0.5 in eight out of the 25 immunotherapy cohorts . SOX10 presented a better predictive value than B clonality, with AUC values over 0.5 in 8 immunotherapy cohorts. However, the prognosticative ability of SOX10 was lower than that of the TIDE (AUC > 0.5 in 18 immunotherapy cohorts), MSI score (AUC > 0.5 in 13 immunotherapy cohorts), TMB (AUC > 0.5 in 8 immunotherapy cohorts), CD274 (AUC > 0.5 in 21 immunotherapy cohorts), CD8 (AUC > 0.5 in 18 immunotherapy cohorts), IFNG (AUC > 0.5 in 17 immunotherapy cohorts), T clonality (AUC > 0.5 in 9 immunotherapy cohorts), and Merk 18 (AUC > 0.5 in 18 immunotherapy cohorts).Then, to thoroughly analyze the prospective merit of SOX10 as a new immune target in pan-cancer, sensitive drugs predicated on SOX10 expression were predicted . We also. We also. We also. We also. At the same time, we identified cell types and used UMAP to make it intuitionistic, which demonstrated 13 cell clusters . Similarly, the co-expression status of different types of cells is shown in . In descending order, the proportion of subtypes in a low-expression cluster of SOX10 was NPC (less than 50%), MES, AC, and OPC. The expression level of SOX10 in different subtypes is shown in the violin plot . It was found that SOX10 had a high expression in neoplastic cells, astrocytes, neurons, oligodendrocytes and oligodendrocyte progenitor cells.Finally, we utilized single-cell sequencing to analyze the circumstances of stratification, identification, and SOX10 co-expression on glioma cells. UMAP determined by Copynumber Karyotyping of Tumors analysis stratified cells into diploid (average) status and aneuploid  status . At the . At the . At the . At the . At the . Moreover, we drew correlation circles for positively and negatively correlated checkpoint genes, growth factors, cytokines, and other genes in the SOX10 high-expression group . For checkpoint genes, we could see a strong correlation in LGALSS of M1 macrophages and HAVCR2 of microglial cells, macrophages, M2 macrophages, and M1 macrophages themselves . Also, HAVCR2 of M1 and M2 macrophages seemed to be regulated by LGALS9 of many cells, such as neoplastic cells, microglial cells, and neural stem cells. As for the growth factors, IGF2 of unknown cells showed robust correlations with IGF1R, IGF2R, and INSER of the other six cell types . Macrophages may interact with microgial cells via CCL4, and interact with T cells and oligodendrocyte progentior cells via IL 1B . The correlation with other genes could be found in Subsequently, we performed enrichment analysis to determine the correlation between different immune regulatory processes and SOX10 expressions. High-expression SOX10 was significantly positively associated with the Notch signaling pathway and migration and activation regulation of the macrophage. In contrast, low-expression SOX10 was positively associated with the activation of MAPK activity, MAPK signaling pathway, and regulation of T-cell migration . Moreove. Moreove. Moreove. Moreove. Moreove. Besides, with the increase in SOX10 expression, neighboring CD68+ cells, CD163+ cells, and CD8+ cells are also increased . The quantity of CD8+ cells at the distance of 0–25 μm and 25–50 μm neighboring SOX10-expressed cells exploded in the Glioma WHO IV group, while the amount of CD68+CD163+ cells also increased. Hence, we concluded that CD68+CD163+ M2 macrophages, and CD8+ T cells, were the prepotent infiltrated immune cell types in glioma. Meanwhile, SOX10 expression is a regulator of neighboring immune cells.We performed multiplex immunofluorescence in the controlled group and different grades of glioma groups to further characterize the relationship between SOX10-expressed cells and neighboring CD68+CD163+ cells, and CD8+ cells. The results revealed that SOX10 expression is elevated with the increase in glioma grades . BesidesIn recent years, studies revolving around gliomas are mostly concentrating on the TME, which is suggested to be one of the main obstacles to improving the prognosis and OS of HGG patients . To explA complex range of genomic alterations also has clinical implications for glioma classification and prognosis. In SNV analysis, several frequent somatic mutations in gliomas, including IDH1, TP53, and ATRX, have been found to present more in cluster2 than in cluster1 , 32. BesThe SOX family has been confirmed to be closely associated with the immune features of the TME. In gliomas, the copious SOX family has played a crucial role in cell differentiation. Also, the SOX family and their mRNA expression levels have been associated with glioma patients’ prognosis . In our An epigenome profiling of GBM indicates that SOX10, an oligodendrocyte forerunner marker and chromatin modifier, is a dominant regulator in RTK I-subtype tumors . It alsoBased on the types of function and activation, macrophages can be divided into two types: M1 macrophages and M2 macrophages . M1 macrGSVA shows a negative association with SOX10 and cardiolipin biosynthesis, citric acid cycle, trans-sulfuration, pyrimidine metabolism, and ubiquinone. SOX10 also has pleiotropic effects in cancer-immune cascade steps. Considering metabolism is a vital determining factor in the survivability and potency of immune cells, SOX10 is supposed to be a more remarkable biomarker in immunotherapy response than B clonality .The SOX family has also been found to have the ability to regulate stem and progenitor cells in adult tissues . Our sinGlioma invasion is driven by autocrine signaling transmitted by secretory factors that signal through receptors on the tumor, including growth factors and cytokines . We haveNotwithstanding, the complete comprehensive information, specific functions, and clarified mechanisms of these SOX families in gliomas and many other diseases have not been fully explored and explained. It is reported that as an oncogene, more than 50% of the cancer patients present NOTCH activation mutations . The actIn conclusion, our study demonstrates the outstanding cluster ability of the SOX family. Cluster2 has a better prognosis and longer OS than cluster1. Concentrating on SOX10, multiple results imply that it has a multifaceted prognostic value in gliomas. In gliomas, SOX10 overexpression corresponds to immune infiltration and bleak prognosis. However, Gefitinib and Nilotinib have more utility in patients with highly expressed SOX10. Except for PD1 and EGFR, our results suggest that the high expression of SOX10 may also correlate with other potential immune checkpoints. Given that, SOX10 has the potential to be an auspicious target for glioma immunotherapy.The original contributions presented in the study are included in the article/Conception and design: GX and QC; Foundation support: QC, GX, and RP; Acquisition and analysis of data: GX, KW, ZW, ZD, XL, WY, and ZL; Interpretation of data: PL and JZ; Drafting the manuscript and revising for submission quality: GX, KW, and QC; All authors; Study supervision: QC and RP. All authors contributed to the article and approved the submitted version.Financial support was provided by the National Natural Science Foundation of China , Hunan Provincial Natural Science Foundation of China (Nos. 2022JJ30971 and 2022JJ20095), Hunan Provincial Health Committee Foundation of China (No. 202204044869), and Xiangya Hospital Central South University postdoctoral foundation.The author express gratitude to the public databases, websites, and softwares used in the paper. We are grateful to the High Performance Computing Center of Central South University for partial support of this work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer DW declared a shared parent affiliation with the authors PL and JZ to the handling editor at the time of the review.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Non-transfusion-dependent thalassemia (NTDT) has been considered less severe than its transfusion-dependent variants. The most common forms of NTDT include β-thalassemia intermedia, hemoglobin E/beta thalassemia, and hemoglobin H disease. Patients with NTDT develop several clinical complications, despite their regular transfusion independence. Ineffective erythropoiesis, iron overload, and hypercoagulability are pathophysiological factors that lead to morbidities in these patients. Therefore, an early and accurate diagnosis of NTDT is essential to ascertaining early interventions. Currently, several conventional management options are available, with guidelines suggested by the Thalassemia International Federation, and novel therapies are being developed in light of the advancement of the understanding of this disease. This review aimed to increase clinicians’ awareness of NTDT, from its basic medical definition and genetics to its pathophysiology. Specific complications to NTDT were reviewed, along with the risk factors for its development. The indications of different therapeutic options were outlined, and recent advancements were reviewed. Thalassemias are inherited hemoglobinopathies characterized by the defective synthesis of α- or β-globin chains of adult hemoglobin A. Alpha and beta thalassemias are labeled after the affected globin chain. Based on clinical presentations and genotypes, thalassemias are classified as thalassemia major, intermedia, or minor phenotype. The clinical classification of thalassemias is currently widely applied in clinical practice related to transfusion requirements . TransfuNTDT includes three clinically diverse forms: β-thalassemia intermedia (β-TI), hemoglobin E/β-thalassemia (mild and moderate forms), and α-thalassemia intermedia (hemoglobin H disease) ,3,4. TheThis review has three objectives that focus on β-TI, the most common subtype of NTDT: first, to explore the genetic factors that contribute to the diverse clinical presentations in patients with NTDT compared with those in patients with TDT; second, to provide an overview of key pathophysiological mechanisms, particularly in the absence of transfusions, and to present how these mechanisms transform into clinical morbidity; and third, to highlight the currently available management outline and novel developments.The severity of NTDT depends on the extent of imbalance between α- and non-α-chains and other genetic factors that alter the natural history of the disease . The div+) to the complete absence of β-globin (β0) [Primary modifiers are the vast diversity of mutations that affect the β-globin gene in the homozygous or compound heterozygous state . There abin (β0) .Secondary modifiers are the genetic abnormalities that modify the degree of α-/β-globin chain imbalance. These include the coinheritance of different mutations in the α- and γ-chains and genes involved in the γ-chain expression ,7. The pTertiary modifiers are mutations external to the globin genes that can modify the phenotype and the propensity to complications . These gThe pathophysiology of NTDT is dependent on the magnitude of α- to β-globin chain imbalance ,3,12. ThThe decreased synthesis of β-globin chains leads to excess free α-globin chains and subsequent hemichrome formation, which precipitate and induce oxidative damage to immature and mature red blood cells (RBCs) ,13,14. TThe sustained anemia and elevated erythropoietin levels stimulate erythroid precursors to release erythroferrone, which suppresses hepcidin expression, the main regulator of intestinal iron absorption in the body ,16,17. TA hypercoagulable state has been described in patients with NTDT ,23,24. VThe accurate differentiation of TDT from NTDT is essential for developing an appropriate management plan for each patient, which may be complicated at presentation . Table .The diagnosis of NTDT is usually established at an older age than that of TDT. The clinical characteristics of patients with NTDT are associated with the severity of chronic anemia; the lower the hemoglobin level, the more likely to develop skeletal deformities, growth restriction, and progressive splenomegaly ,30. PatiDespite the high hemoglobin levels and transfusion independence, most thalassemia-related complications occur more frequently in patients with NTDT than in patients with TDT ,32. The Hemosiderosis secondary to increased iron absorption and iron-induced organ dysfunction is a significant issue in NTDT ,13,36. T9/L, nucleated RBCs >300 × 106/L, low HbF levels, and history of thrombotic events [The hypercoagulable state in NTDT is associated with a prevalence of thromboembolisms (TEs), ranging from 3.9% to 14% ,43. TEs c events ,42,44,45EMH is almost exclusive to NTDT compared with TDT . EMH is 6/L, and history of TE [The prevalence of PHT ranged from 4.8% to 59% in patients with NTDT, depending on the cohort studied and the method of diagnosis ,50,51,52ry of TE ,42,50,53Bone abnormalities in NTDT are multifactorial, including the following: sustained IE, subsequent bone marrow expansion, iron overload, and genetic factors ,13,54,55Leg ulcers were reported to occur in 7.9% of patients with β-TI and up to 22% of patients with hemoglobin E/β-thalassemia ,59. TherAlloimmunization is significantly influenced by age since it tends to be more prevalent in older patients than in those who received transfusions when younger . A GreekOther complications in NTDT include growth retardation and delayed puberty, gallstones, and decreased quality of life ,6,13,54.The management of NTDT requires a multidisciplinary approach focused on the patient’s clinical course, as prevention and early diagnosis of complications prevent morbidity and poor quality of life . WithoutAlthough regular transfusions are the foundation of medical therapy in TDT, the time to initiate transfusion in NTDT is a challenging decision. The indication for transfusion should not be based on hemoglobin level alone but also on symptoms of anemia and NTDT-related complications ,65. To iAn increase in spleen volume is observed in patients with NTDT, regardless of transfusion requirements, which may subsequently lead to hypersplenism ,30. The All patients with NTDT should be screened for iron overload starting at the age of 10 years when iron-related complications are anticipated to emerge . The samThe drugs currently available for clinical use in iron chelation include intravenous or subcutaneous deferoxamine, oral deferiprone, and oral deferasirox . DespiteThe α- to β-globin chain imbalance may be improved with drugs that increase the production of gamma-globin chains, which bind to the excess α-chains, increasing HbF levels and improving erythropoiesis . The besSeveral other small and nonrandomized clinical trials with different HbF-modifying agents were conducted in patients with thalassemia, including thalidomide, decitabine, azacytidine, sodium phenylbutyrate, and arginine butyrate. The use of recombinant human erythropoietin (rHuEPO) or the newer erythropoietic-stimulating agent darbepoetin alfa in NTDT is associated with an increase in total hemoglobin levels and may function as a survival factor for mature RBCs by prolonging their lifespan ,82,83. WThe number of novel medications for NTDT in clinical studies indicates the rapid evolution of management of these patients ,86,87. THematopoietic stem cell transplantation (HSCT) is a potentially curative treatment for TDT, and disease-free survival rates greater than 90% have been reported in patients with good risk characteristics according to the Pesaro criteria ,87. The The combination of gene therapy and autologous HSCT exemplifies a future therapeutic option for thalassemias ,86. HoweGene therapies show significant promise for improving patients’ lives with thalassemia but still require further long-term studies. Unfortunately, gene therapies are beyond the reach of many patients as these techniques currently require sophisticated and expensive resources.Recent studies have implicated the JAK2 and transforming growth factor (TGF)-β superfamily in the control of erythropoiesis . ErythroLuspatercept is a novel recombinant protein that preferentially binds to TGF-β superfamily ligands, such as GDF11, GDF8, and activin B, in vivo . The sigStudies on pyruvate kinase (PK) in mouse models have revealed that metabolic disturbances in PK deficiency influence the maturation of erythroid progenitors leading to IE, in addition to altering RBC survival . MitapivRuxolitinib is an approved drug for polycythemia vera and myelofibrosis caused by activated JAK2 mutations . Mouse mIn the pathophysiology, the importance of decreased hepcidin in iron overload leading to increase ferroportin was reviewed, suggesting that an increase in the concentration of hepcidin or decrease in the ferroportin level may preclude excessive iron absorption . The preTMPRSS6 is a transmembrane serine protease that reduces the production of hepcidin . Murine VIT-2763 is a ferroportin inhibitor that competes with hepcidin for binding to ferroportin . StudiesThe patients with NTDT can have a variety of clinical presentations, and the diagnosis can occur at any age. An acute presentation of NTDT can lead to a clinical dilemma, and patients, particularly those with β-thalassemia intermedia and high HbF levels, may be initially diagnosed with TDT. Follow-up evaluations of hemoglobin levels would help determine whether the patient has TDT or NTDT.Once a diagnosis of NTDT is established, the baseline hemoglobin levels and growth rate will determine the frequency of follow-up in the clinic until 10 years of age. Thereafter, patients will need follow-up assessments of serum ferritin levels and complications every 3 months. Follow-up assessments of iron overload by serum ferritin measurement and liver MRI (if available), as well as initiation of iron chelation as per recommendations, are crucial and can prevent complications.Blood transfusion is indicated during episodes of acute stress. If the patient shows evidence of progressive changes in childhood or complications of NTDT, a regular blood transfusion regimen can be initiated. Once the required outcome is achieved, transfusions can be tapered or withdrawn. Splenectomy is considered in very specific situations, as the risk of complications increases after splenectomy. A trial of hydroxyurea can be initiated for patients with low baseline hemoglobin levels, frequent transfusions, or complications for a 6-month period. If no response is detected, the medication should be discontinued. Screening for complications of NTDT, including echocardiography for tricuspid valve regurgitation in PHT, bone mineral density evaluation for osteoporosis, and leg examination for ulcers, can lead to early detection and management.Patients with NTDT in the past were considered to have a less severe disease; however, it is now known that it has a distinct morbidity profile that typically manifests later in life and negatively impacts the quality of life. The improved understanding of the pathophysiology of NTDT and the development of complications has helped ameliorate disease management and develop a plan for the development of novel therapies. The development of the TIF guidelines for NTDT has facilitated constructing a screening and management plan in the presence of the diverse clinical picture of NTDT. Early medical intervention is crucial for preventing long-term complications that may reach the point of irreversibility.


Obsessive–compulsive disorder (OCD) a complex neuropsychiatric disorder, is characterized by irresistible obsessive thinking and compulsive behavior. Folate is a member of water-soluble vitamins in the human body and sustains many normal daily activities . Homocysteine, a sulfur-containing non-essential amino acid, has been investigated in numerous psychiatric disorders . Vitamin B12 is a type of complex organic compound with cobalt contained. Moreover, vitamin B12 and folate deficiency and high levels of homocysteine were found to have an effect on brain functions and also lead to non-specific psychiatric symptoms.This study aimed to confirm the epidemiological evidence of OCD and investigate whether vitamin B12, folate, and homocysteine have an effect on the etiology of OCD.A systematic search was conducted on eight databases , and the retrieval time was up to March 2021. The available articles involving patients with OCD with/without abnormal serum levels of vitamin B12, folate, and homocysteine were comprehensively reviewed and analyzed.p = 0.794). And serum homocysteine was significantly higher in the patients with OCD . Vitamin B12 was significantly lower in patients with OCD .A total of 5 studies involving 309 patients were included in this meta-analysis, including 172 cases in the experimental group and 137 in the control group. The content of folate in the OCD group was not significantly different from that in the control group . According to the latest revision of diagnostic criteria for OCDA series of articles primarily demonstrated that a subset of the different forms of OCD is familial and thus might arise from genetic factors –8. A metAmounts of evidence support the cognitive behavioral therapy and selective serotonin reuptake inhibitors (SSRIs) have been as the treatment of OCD –26.As revealed, homocysteine is correlated with both current depression and a history of depression, and a higher concentration of homocysteine might increase the risk of depression . AmountsThis meta-analysis was based on the statement of the Preferred Report Item for Systematic Reviews and Meta-analysis (PRISMA) . A systeOriginal articles matching the following eligibility criteria were included: (1) cross-sectional, case-control, or cohort study design; (2) involving a sample of patients with clinically diagnosed OCD, with/without a comparison group (at least 10 people in each group); (3) reported vitamin B12, folate, and homocysteine as outcomes and diagnosed with the Structured Clinical Interview for DSM-4/DSM-5; (4) provided sufficient raw data for calculation; (5) had clear inclusion and exclusion criteria  for participants; (6) published in English or Chinese. The exclusion criteria included: (1) reviews, letters, case reports, or conference abstracts; (2) duplicated or overlapping data. All sifting process was conducted by two researchers  independently. Their respective evaluation results were compared, and any discrepancies were resolved by discussion.In total, three independent researchers  extracted the information from eligible articles. Baseline data extracted from the respective manuscripts and supplements are presented later: the first name of the author, year of publication, country, ethnicity, study design, sample size, mean age, gender ratio, comparison group, diagnostic scales, and also diagnostic criteria. To accurately evaluate the methodological quality of eligible articles, three researchers  employed the Newcastle-Ottawa Scale (NOS)  for the 2 statistic were performed to identify heterogeneity among articles. Under insignificant heterogeneity (I2 <50% or p > 0.1), a fixed-effect model was used to estimate the pooled effect size; otherwise, the random-effect model was used. To determine potential sources of heterogeneity between articles, sensitivity analysis, and meta-regression were conducted using prespecified variables . The Egger's test and Begg's test were performed to detect publication bias. Furthermore, all the data synthesis and analysis were conducted using Stata version 11.0 .The odds ratio (ORs) for dichotomous data was estimated, and standard mean differences (SMDs) for continuous data with the corresponding 95% CIs were also estimated. The chi-squared test and IA total of 1,426 records were found in the initial search strategy , and 127 duplicates were removed. A total of 1,299 records were excluded for irrelevant themes or unsuitable types of articles after the titles and the abstracts were screened. In accordance with the eligibility criteria, 40 articles were excluded through a careful review of the full texts. In summary, 5 articles involving 172 cases of OCD were selected for the meta-analysis .n = 4) reached 6 or higher , DSM-4, and DSM-4-TR, were employed separately in included articles to evaluate anxiety disorder. On the whole, 7 different validated screening scales were used for evaluating obsessive symptoms. The Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) is the most common tool. Furthermore, there was a problem that thresholds adopted to screening scales might differ from each other in different articles when defining anxiety symptoms.p = 0.794, p = 0.000, I2 = 87.6%, Data regarding serum levels of folate were provided in 4 studies with a total of 309 patients were analyzed –40, 43. p = 0.001, p = 0.020, I2 = 74.3%, Data regarding serum levels of homocysteine provided in 3 studies with a total of 185 patients were analyzed –39. The p = 0.001, p = 0.117, I2 = 49.1%, Data regarding serum levels of vitamin B12 provided in 4 studies with a total of 110 patients were analyzed –40, 43. There was no obvious influence of one individual study on the pooled SMDs of serum levels of folate, vitamin B12, and homocysteine in groups –7.Obsessive–compulsive disorder is a complex neuropsychiatric disorder with not fully elucidated etiology , 11, 21.via activity at the N-methyl-D-aspartate glutamate receptors  some articles' measures data are not distributed normally and were not reported in the form of median and quartile, and therefore, they could not be included in the meta-analysis; (2) only English and Chinese language reports have been searched and maybe consequently leads to missing data from other vital high-quality articles published in other languages; (3) since all the included studies were retrospective with small sample sizes, high-quality RCTs with large sample is lacking; (4) the results were based on unadjusted estimates, more accurate outcomes would be achieved due to adjustments for other confounding factors ; (5) some factors had not been taken consider into study, e.g., renal function of participants, dietary habits changes correlated with symptoms in patients with OCD, reduced ultraviolet exposure required for vitamin D synthesis; (6) one set of trails from the last century has not mentioned the details about the diagnosis and detection methods ; (7) mosIn conclusion, this meta-analysis found that OCD might be correlated with a low level of vitamin B12 and high level of homocysteine, whereas the sample here was too small to conclude that this change might be a vital biological indicator for OCD. Abnormal serum levels of vitamin B12 and homocysteine expression might be a risk factor for the development of OCD. Clinicians should be vigilant about serum levels of vitamin B12 and homocysteine in patients with OCD, which should be investigated in more in-depth and high-quality studies. These findings can provide a starting point for further research.The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s.SY: conceptualization, methodology, article selection, data extraction, data analysis, quality assessment, and writing—original draft preparation. HL and YY: data extraction and quality assessment. NH: quality assessment. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Through WGCNA analysis and LEFSE analysis, it was found that the core bacteria significantly related to NH4+-N were basically distributed in the intertidal area. For example, Nitrosomonadaceae, Nitrospira and Sphingomonas were the core bacteria showed significant different abundance in the intertidal area, which have the ability to metabolize NH4+-N. Our findings suggest that NH4+-N plays an important role in soil bacterial community structure in the Arctic meltwater areas.Global climate change has caused the changes of the ecological environment in the Arctic region, including sea ice melting, runoff increase, glacial lake expansion, and a typical meltwater area has formed in the Arctic coastal area. In this study, the meltwater areas near six different characteristic areas of Ny-Ålesund in 2018 were taken as the research objects, and high-throughput sequencing of V3–V4 regions of all samples were performed using 16S rDNA. Among the soil samples of six glacial meltwater areas in Ny-Ålesund, Arctic, the meltwater area near the reservoir bay had the highest bacterial abundance, and the meltwater area near the sand had the lowest one. The dominant phyla in soil samples were Proteobacteria, Actinobacteria, Acidobacteria. The NH Climate change has a profound impact on the global landscape, especially in the Arctic, where the direct human impact is very small ,2. One oThe increase of lakes and bare soils caused by climate change in the Arctic region will lead to corresponding changes in the ecological environment in this region. As an important part of the ecosystem, microorganisms not only play the role of decomposers, but are also affected by the physicochemical properties of the environment and nutrients. Important physicochemical factors affecting the structure of microbiomes include organic carbon, water content, pH, phosphorus ,8,9, redFew previous studies have compared the intertidal soil with the subtidal soil, while most studies in the Ny-Ålesund area have focused on soil, sediment or topsoil in a single characteristic area ,15,16. AIn this study, in order to study the differences of soil bacterial communities in intertidal and subtidal zones of different meltwater regions and the influence of soil physicochemical factors, six meltwater areas with different characteristics were selected from the Ny-Ålesund, Arctic and the soil samples of intertidal and subtidal zones were collected. The bacterial diversity composition of soil samples was analyzed by high-throughput sequencing of 16S rRNA V3–V4 gene. Meanwhile, the physicochemical properties of these soil samples were determined by conventional instrumental analysis to understand their influence on the composition of soil bacterial communities and to explore the obvious influence factors.The study area is located in the Ny-Ålesund region in the northwest of Svalbard Archipelago in the Arctic. Under the influence of the North Atlantic warm current, the climate in this region is relatively warm and humid. It is one of the regions with the highest temperature in the same latitude in the Arctic, and also one of the regions with the fastest warming in the Arctic . Due to 4+-N, SiO42−-Si, NO3−-N, NO2−-N, and PO43−-P was as follows [The determination of soil physicochemical properties included 9 items. Soil moisture content (MC) was determined by drying at 105 °C to constant weight. Soil pH was measured by adding 10 mL of distilled water to 4 g of soil and then using a pH meter . OrC and OrN were determined by the following procedure : First,  follows : After l−1) of the soil sample was extracted according to the instructions of the Mobio PowerSoil DNA Isolation Kit  [® high-fi PCR Master Mix , 0.2 μmol/L−1 of primers 341F and 806R [The total DNA  . A 30 µLand 806R ,23, 10 nHigh-throughput sequencing of the 16S rRNA gene V3-V4 region was performed on an Illumina MiSeq platform. Clean Data were obtained by concatenation and quality control to remove low-quality sequences. QIIME  , UCHIME https://www.r-project.org/, accessed on 24 August 2022) package was used to draw the core flora network map and combined with the results of LEfse analysis, the influence of physicochemical factors on the flora structure was analyzed.Software Qiime 1.7.0  was usedNine physicochemical properties, including moisture content, pH, organic carbon, organic nitrogen, and five soluble nutrients  of soil samples in 12 sites  were measured . It coulOryzihumus and Alcaligenaceae uncultured in the intertidal zone was higher than that in the subtidal zone in all sampling sites, while that of Thiobacillus was on the contrary.Thirty-six samples from six meltwater areas in Ny-Ålesund, Arctic were high-throughput sequenced, and an average of 58,012 sequences were obtained from each sample for subsequent analysis. The effective sequences of all samples were clustered into OTU according to the sequence similarity of 97%, and a total of 19,258 OTUs were obtained. On average, there were 3105 OTUs in each sample and 9316 OTUs in each sampling site. OTUs of all samples were clustered into 59 phyla, among which Proteobacteria, Actinobacteria, Acidobacteria, Gemmatimonadetes, Bacteroidetes, and Chloroflexi were the phyla with high relative abundance . Among tAt the same time, through Alpha diversity analysis, it can be seen that the Good’s coverage index of 36 soil samples ranges from 96.0% to 98.7%, with the sufficient sequencing depth. ACE was in the range of 2169.978–5346.601, Chao1 index was in the range of 2057.329–5212.196, Shannon index was in the range of 4.523–6.985, and Simpson index was in the range of 0.003–0.143. In all samples, the values of Chao1 (2169.978) and ACE (2057.329) of SZX1 were the lowest, and the Shannon index was 5.269, which proved that the community structure of SZX1 was relatively simple. The Shannon index of KZX3 is 6.919, which was larger than that of all samples, and the Simpson index (0.003) of which was the lowest. The values of Chao1 (5212.196) and ACE (5346.601) of KZX3 were the largest, which proved that the microbial diversity of KZX3 was high, and the population was rich and evenly distributed.4+-N, SiO32−-Si and MC had great influence on the overall diversity and distribution of bacterial communities. Among all sampling sites, BZS and BZX were most affected by three physicochemical factors , SiO32−-Si  and NH4+-N  were significantly correlated with the bacterial communities of soil samples  was used to explore the relationship between bacterial diversities and nine soil physicochemical factors. The results showed that NH factors . At the  samples .4+-N and SiO32−-Si. The results obtained from module MElightcyan was imported into the Cytoscape to get a network diagram  and the identification of 19,258 OTUs reveal a surprising diversity of bacterial communities in these meltwater areas. From high-throughput sequencing data of the 16S rRNA V3-V4 gene region, we found that most bacterial taxonomic groups belong to common phyla, including Proteobacteria, Actinobacteria, Acidobacteria, Gemmatimonadetes, Bacteroidetes, Chloroflexi. Proteobacteria, Actinobacteria and Acidobacteria, with relatively high abundance, were also frequently found in low temperature environments besides the Arctic, such as the Antarctic, the Tibetan Plateau and Siberia ,32,33,3432−-Si  and NH4+-N  were significantly correlated with the bacterial communities in all the six meltwater areas. It is obvious that among them, the moisture content in the subtidal area was higher than that in the intertidal area. The high correlation of SiO32−-Si may be caused by erosion and leaching of sedimentary rocks and sand by meltwater at the glacial front, forming a high concentration at the bottom of the meltwater area [In the Arctic, physicochemical factors play an important role in the change of soil bacterial community diversity. Although we observed some differences in physicochemical factors in soil samples from meltwater areas with different characteristics, MC , SiOter area . Howeverter area  and in oter area . The gen42−-Si were two main influential factors determined by network analysis. Most importantly, we revealed the physicochemical factors influencing the intertidal and subtidal microbial communities by establishing relationships between subnetworks and physicochemical factors, and by identifying core bacterial communities significantly associated with ammonium and their main sampling sites. Among all the dominant bacteria, Proteobacteria was the most abundant one, among which ammonia-oxidizing bacteria consume NH4+-N at a slower rate, and the adsorption capacity of NH4+-N is stronger in the intertidal area. Therefore, it was observed that the NH4+-N content in the intertidal areas was higher than that in the subtidal areas [32−-Si, we found that the core bacteria genera which were strongly correlated with NH4+-N had significant differences in the intertidal area combined with the bacteria with significant different abundance at sampling sites analyzed by LEFSE. For example, the core bacterial genera with significant differences in SZS belong to Nitrosomonadaceae and Nitrospira. Nitrosomonadaceae belongs to β-proteobacteria and is a kind of aerobic ammonia-oxidizing bacteria [Nitrospira is an ammonia-oxidizing bacteria that uses AMO and HAO [Sphingomonas, the core bacteria with significant difference in WZS, is abundant in marine sediments and terrestrial soil, and often plays an important role in the denitrification process of industrial wastewater (containing nitrogen) and domestic sewage and the pollution removal of contaminated water sources [4+-N on changes of intertidal and subtidal bacterial communities in both local (module) and global  aspects.In addition, local soil factors, rather than global factors, have been identified as the main direct formative force of the Arctic microbiome ; local sal areas . Althoug and HAO ,44 to ox sources . The mod4+-N is an important explanatory factor for intertidal and subtidal microbial communities. Although different meltwater areas have different physicochemical factors and dominant bacterial communities due to their different ecological environment, NH4+-N is the influential factor of the bacterial community in intertidal and subtidal areas in all meltwater areas. As global warming intensifies, lakes and ponds of arctic glacial meltwater will gradually increase, so changes in physicochemical factors and microbial structures will become increasingly important.We observed significant differences in physicochemical factors and bacterial community diversity between intertidal and subtidal areas and between different meltwater areas. NH