dataesr/scientific-paragraphs-categorization huggingface dataset:
What study protocol for phase III RCTs with SCs preparations? It would be preferable to perform phase III RCTs using a standard protocol, but this is not currently available. A previously proposed frailty protocol is the one used in the phase III RCT NCT02582138 (SPRINTT: Multicomponent Intervention for Physical Frailty and Sarcopenia)  (Marzetti et al. , 2018; Bernabei et al. , 2022)  (Figure  3 ). SPRINTT was designed to test interventions in older people with physical frailty and sarcopenia. The primary outcome is the ability to independently walk 400 m in <15 min. Inclusion criteria are: 1) short physical performance battery score between 3 and 9, 2) low lean appendicular mass, and 3) ability to independently walk 400 m  (Marzetti et al. , 2018; Bernabei et al. , 2022). SPRINTT has the advantage of including the assessment of sarcopenia as an inclusion criterion.
Exosome therapies are intensively investigated in various clinical development programs. ClinicalTrials. gov lists 166 exosome intervention trials, but none for aging. The Chinese clinical trial database (chictr. org. cn/searchprojen. aspx) included a clinical trial with MSC-derived exosomes for photoaging (ChiCTR2200061216; see Section 3. 2. 3. 2; Table  2 ). Interestingly, the exosomes used were loaded with circular RNA, a genetic modification procedure that can improve therapeutic efficacy [for information on genetic modification, preconditioning and engineering of MSC-derived exosomes, see  (Ahmed and Al-Massri, 2022; Chen et al. , 2022) ]. Yoshida et al. (2019)  reported that administration of nicotinamide phosphoribosyltransferase (NAMPT)-containing exosomes significantly enhanced wheel-running activity and prolonged lifespan in aged mice [for review of studies of MSCderived exosomes in preclinical models of age-related diseases, see  (Ahmadi and Rezaie, 2021) ; for perspectives and challenges of clinical trials with exosomes, see  (Rezaie et al.
Repurposing of approved SC products and related agents for aging frailty Repurposing medicinal agents means finding new therapeutic indications for existing ones  (Ayyar and Subramanian, 2022). Repurposing is a cost-and time-effective mechanism that can be applied to develop new SC therapies for aging frailty  (Begley et al. , 2021). Hemacord (BHI Therapeutic Sciences, United States) is an FDA-approved HPC cord blood product for disorders affecting the hematopoietic system  (FDA, 2022). Hemacord is now in clinical development to be repurposed for acute ischemia stroke (NCT03735277), and could also be repurposed for aging frailty (Figure  4 ). It is important to mention that a large number of clinical studies are underway with allogeneic umbilical cord blood infusion for stroke and several other (non-hematopoietic) therapeutic indications  (Ray and Mukherjee, 2021; Paton et al.
Ongoing clinical trials for facial skin aging 4. 2. 2. 1 hSVF preparations for facial skin aging Two RCTs (NCT03928444 and RPCEC00000362) and one single-arm clinical trial (IRCT20141007019432N2) are investigating hSVF preparations for facial rejuvenation (Table  2 ). The positive results obtained by  Yin et al. (2020)  with autologous hSVF transplantation suggest that similar protocols could be used in other clinical settings for regulatory purposes (see Section 4. 2. 3).
Weakness of clinical research with hAD-MSCs for facial skin aging 4. 2. 3. 1 SVFs are regulated as biologicals in the US A large number of studies with SVFc for facial rejuvenation have been conducted in the US  (Surowiecka and Struzyna, 2022) , but very few are registered on ClinicalTrials. gov (Table  2 ). One important reason is that the FDA regulates SVF preparations as biologics, because mechanical processing is required  (ASCS, 2019). This means that any surgeon wishing to use SVF preparations must submit an Investigational New Drug Application (INDA) to the FDA and be approved by an ethics committee. Consequently, the FDA initiated legal action against SC clinics using unauthorized SC products  (El-Kadiry et al. , 2021).
Regulatory aspects of clinical trials with autologous SVFs preparations The FDA regulates allogeneic SVF products as biologics (ASCS, 2019), but the risk of skin rejection prevents clinical development of allogeneic SVF preparations for facial rejuvenation. The final consequence is that the very numerous US clinical trials with autologous SVF preparations for facial rejuvenation  (Surowiecka and Struzyna, 2022)  are not registered on ClinicalTrials. gov. Some RCT with autologous SVF preparations have recently been registered on ClinicalTrials. gov (see Table  2 ). The application of similar RCT protocols could pave the way for autologous SVF preparations to be registered on ClinicalTrials. gov and comply with FDA regulations (Investigational New Drug applications and FDA-approvals).
There are two ongoing clinical trials for cutaneous photoaging: 1) NCT01771679 which was evaluating intravenous allogeneic hBM-MSCs for the treatment of facial photoaging (recruitment was suspended), and 2) ChiCTR2200061216 which is currently exploring whether hADSC-derived exosomes (loaded with circcol elns) can promote collagen and elastin synthesis in photoaged skin (Table  2 ). Circular RNAs (circRNAs) include a large family of non-coding RNAs, which can regulate gene expression (acting on transcription, mRNA turnover and translation, by sponging microRNAs and RNA-binding proteins)  (Panda, 2018). Intensive research of noncoding RNA therapy for photoaging is currently being carried out at Sun Yat-Sen University Hospital (China) (ChiCTR2200061216, Table  2 )  (Peng et al. , 2017; Hou et al.
*Correspondence to: Dr. Ziv Gan-Or, Department of Neurology and Neurosurgery McGill University, 1033 Pine Avenue, West, Ludmer Pavilion, room 312 Montreal, QC; E-mail: ziv. gan-or@mcgill. ca Relevant conflicts of interest/financial disclosures: Nothing to report. Funding agencies: This study was financially supported by grants from The Michael J. Fox Foundation, the Canadian Consortium on Neurodegeneration in Aging (CCNA), the Canada First Research Excellence Fund (CFREF), awarded to McGill University for the Healthy Brains for Healthy Lives initiative (HBHL), and Parkinson Canada. The Columbia University cohort is supported by the Parkinson's Foundation, the National Institutes of Health (K02NS080915 and UL1 TR000040), and the Brookdale Foundation.
Targeted Next Generation Sequencing The ARSA gene was sequenced in the four cohorts collected at McGill University with targeted next generation sequencing by molecular inversion probes (MIPs) as previously described. 19 All MIPs that were used to sequence ARSA are provided (Supplementary Table  S2 ) and the full protocol is available at https://github. com/ gan-orlab/MIP_protocol. The library was sequenced using Illumina NovaSeq 6000 SP PE100 platform at the Genome Quebec Innovation Centre. Alignment was performed with Burrows-Wheeler Aligner (hg19)  20  and Genome Analysis Toolkit (GATK, v3. 8) was used for post-alignment quality control and variant calling. 21 e performed quality control by filtering out variants and samples with reduced quality, using the PLINK software v1. 9. 22 Single nucleotide polymorphisms were excluded from analysis if missingness was more than 10%.
Data Quality Control and Analysis in AMP-PD and UKBB Quality control procedures of whole genome sequencing for AMP-PD cohorts were performed on individual and variant levels as described by AMP-PD (https://amp-pd. org/whole-genome-data and detailed elsewhere). 23 Quality control of UKBB whole exome sequencing data was performed using GATK, v3. 8. In this analysis, we used previously suggested filtration parameters for whole exome sequencing data with minimum depth of coverage 10Â and GQ 20. 24  Additionally, all multi-allelic sites were removed from the dataset during the quality control process. Alignment of AMP-PD and UKBB data were performed using the human reference genome (hg38) and coordinates for the ARSA gene extraction were chr22:50,622,754-50,628,152. We performed additional filtration procedures using the UKBB and AMP-PD cohorts to exclude non-European individuals (UKBB field 21,000) and filtered by relatedness to remove any first and second-degree relatives.
Discussion In the current study, we report a possible association between rare functional and loss-of-function ARSA variants and PD. In four of our cohorts, we also identified a possible association between all rare and nonsynonymous variants and PD. 3] [14]  Although the associations described in the present study do not survive correction for multiple comparisons, the fact that there were many nominal associations in independent cohorts may suggest that these associations are real. A recent large-scale burden analysis found an association between rare ARSA loss-of-function variants and PD. 10  This study included data from AMP-PD and UKBB that were also used in our analysis. However, our study included four independent cohorts with no overlapping samples comprising of 2,858 cases and 1,989 controls, and we demonstrated an association of rare ARSA variants with PD in three of four of our cohorts.
1 ). The link between ARSA and PD is not as strong as between GBA1 and PD and only evident in large-scale burden analysis (Supplementary Table  S10 ). Potentially, it could be because of rarity of ARSA variants that associated with PD and could depend on the ethnicity. Our study has several limitations. In some of our cohorts, patients, and controls were not matched for sex and age, which was, therefore, adjusted in the statistical analysis. Different quality control procedures were used for targeted sequencing, whole-exome sequencing, and whole-genome sequencing data, using varying thresholds for depth of coverage and quality assessment as recommended for the different platforms. Notably, lower depths of coverage and GQ scores were used in the UKBB dataset because of methodological differences in whole exome sequencing compared to the other sequencing methods.
Acknowledgments: We thank the participants in the different cohorts for contributing to this study. This study was funded by the  Michael J. Fox Foundation , The  Canadian Consortium for Neurodegeneration in Aging (CCNA)  and the  Canada First Research Excellence Fund (CFREF)  by a grant given to  Healthy Brain  for  Healthy Lives  (HBHL). This research used the NeuroHub infrastructure and was undertaken thanks in part to funding from the  Canada First Research Excellence Fund , awarded.
1 ). This substance has been noted in the past as a potential seizurogenic agent that interacts with GABA A Rs  (Squires and Saederup 1993). We also mined a FAERS dataset to connect drugs with pharmacovigilance-derived seizure alerts. In total, we deduced several candidate AOPs from the findings and propose this framework to connect GABA A R targeting molecules with preclinical seizure alerts, providing testable hypotheses.
Site 8 and site 11 are located within the channel pore. This summary of binding sites resulted from a superposition of atomic resolution structures of GABA A R and homologous proteins. The PDB files used, citations, as well the full description of the ligands occupying the binding sites can be found in Supplementary Table  S1. Note that structural data for the intracellular domain is lacking Vol. : (0123456789) electron microscopy (Cryo-EM) resolved diazepam and flumazenil GABA A R bound structures (PDB IDs: 6HUP  (Masiulis et al. 2019 ) and 6D6T  (Zhu et al. 2018) , respectively. Since NAM effects at α1-containing benzodiazepine sites are seizurogenic  (Crestani and Rudolph 2015) , but not those at α5-containing benzodiazepine sites, subtype information is highly relevant for this class of sites as well (Fig.
The GABA molecule is rendered in cyan sticks. More details are provided in Supplementary Figure  S3. B Chemical feature interactions of the bicuculline bound 6HUK structure rendered with LigandScout 4. 4 Expert. Yellow spheres, blue stars, and red vectors represent hydrophobic, positive ionizable, and hydrogen bond acceptor interactions, respectively. C Side view of the benzodiazepine binding site (α + /γ -interface) from a PDBeFold superposition of selected atomic resolution structures (PDB IDs: 6HUP-diazepam, 6HUO-alprazolam, 6D6T/6D6U-flumazenil). The subunits are rendered individually for more clarity, and the variable positions are highlighted as in panel A with lilac for the principal and light green for the complementary face, respectively. The insert box in the middle depicts the binding modes of diazepam (red), alprazolam (blue), and flumazenil (yellow).
We took advantage of the NeuroDeRisk IL Profiler V1. 0 to profile 81 drugs (111 chemical structures, see "Methods" and Supplementary Table  S3 ) selected from public databases against 8 GABA A R models. The development of the models is fully described in the publically available documentation (https:// docs. intel igand. com/ ndr/ models/). The results involving models for GABA A -related positive allosteric modulation (PAM), similar to that of diazepam, and seizure risk (allosteric inhibition, channel blocking, orthosteric site antagonism) are shown in Table  1. None of the drug structures profiled hit the GABA A ACAMPROSATE 1 ALPRAZOLAM 1 AMOBARBITAL 1 AMOXAPINE 2 BARBITAL 1 BROMAZEPAM 1 BUTOBARBITAL 1 CARISOPRODOL 1 CHLORDESMETHYLDIAZEPAM 1 CINOLAZEPAM -1 2 CINOLAZEPAM -2 1 CLOBAZAM 1 CLORAZEPATE DIPOTASSIUM 1 DESFLURANE 1 DIAZEPAM 1 ENFLURANE 1 ESTAZOLAM 1 ESZOPICLONE 1 FLUDIAZEPAM 1 FLURAZEPAM 2 FLUTAZOLAM 2 HALAZEPAM 1 HALOTHANE 1 HEXOBARBITAL 1 ISOFLURANE 1 LORAZEPAM 1 LORMETAZEPAM 1 MEPROBAMATE 1 METHARBITAL 1 METHOXYFLURANE 1 NORDAZEPAM 1 OLANZAPINE 1 OXAZEPAM 1 PENTOBARBITAL -1 1 PENTOBARBITAL -2 1 PRAZEPAM 1 QUAZEPAM 1 RILMAZAFONE 2 SECOBARBITAL 1 SEVOFLURANE 1 TEMAZEPAM 1 THIOPENTAL 1 TOPIRAMATE 2 TRIAZOLAM 1 ZOPICLONE 1 orthosteric site agonist models, while flumazenil and brexanolone (neurosteroid) were correctly identified by the NDR-IL-Flumazenil-6d6t and NDR-IL-GABA-A-NSteroid-Pregnanolone-5o8f models, respectively (Supplementary Figure  S9 ).
Benzodiazepine drugs in the dataset, including clobazam, were correctly identified by the NDR-IL-GABA-A-PAM-Diazepam-6hup model and were consistent with the anticipated PAM diazepam binding site (highest fit score by diazepam). Several other benzodiazepine drug chemical structures used as queries, such as flutazolam, ketazolam, medazepam, and mexazolam, were not flagged as hits by the NDR-IL-PAM-Diazepam-6hup model (Supplementary Figure  S9 ). However, medazepam is a prodrug, exerting its GABA A R effects through the active metabolites diazepam, nordazepam, temazepam, and oxazepam, all of which were identified by the model (Table  1 ). Similarly, active metabolites of flutazolam, ketazolam, mexazolam, and rilmazafone (flurazepam, diazepam, chlorazepam, and 8 -chloro-6-(2-chloro- phenyl)-N,N-dimethyl-4H-1,2,4-triazolo(1,5-a) (1,4) benzodiazepine-2-carboxamide, respectively) were identified by the model as well.
Carisoprodol (musculoskeletal pain) and meprobamate (an active metabolite of carisoprodol) both hit the NDR-IL-GABA-A-gs-Antag-LB model, the latter with a higher fit score. The highest scoring hit for the Antagonist-6huk model (NDR-IL-GABA-A-gs-Antag-6huk-3) was amoxapine, while the fit to the orthosteric site Antagonist-LB model (NDR-IL-GABA-A-gs-Antag-LB) scored even higher. Additionally, it was the only drug in the dataset that hit both orthosteric antagonist models, and not any other GABA A R models in the study. Antagonists of the orthosteric GABA/bicuculline binding sites are known to be toxic and induce seizures, and thus, hits were not expected to be found among the selected test compounds which are all approved for use in humans. Nevertheless, the amoxapine drug package inserts and pharmacovigilance data indicated potential seizure risk though no indication of the mechanism of action or target.
Pharmacovigilance alerts for seizure/convulsion AEs of the compounds In addition to the NeuroDeRisk IL Profiler in silico screen to predict GABA A R molecular initiating events, all 81 compounds were subjected to a data search in the FAERS database as described in the "Methods" section. We retrieved from the entire set of the used records all those which contain any of the Med-DRA terms from the neurological seizure/convulsion group and filtered on the basis of the disproportionality analysis as described in the methods (Supplementary Figure  S10 ). A total of 46,285 records reflecting seizure-type AEs for 41 of the screened drugs passed the statistical criteria (Supplementary Figures  S11  and S12 ) and went into further analysis. Figure  4  displays the contribution of each AE to the total AEs of a given drug (Fig.
4 , upper part) and the contribution of each drug to the total pool of seizure-category AEs in the entire dataset (Fig. 4 , lower part). The top 20 AEs account for > 98% of the 46,285 records; only these are displayed in Fig. 4. Of the total pool of drugs known to exert effects on GABA A Rs, 43% are associated with AEs from the seizures/convulsions category at the level of stringency used for the analysis. Of note, of the 43 benzodiazepines for which FAERS records were available, we found seizure/convulsion AE alerts for 18 of them, and two Z-drugs zopiclone and zolpidem, and the benzodiazepine antagonist flumazenil. The chemically atypical 1,5-benzodiazepine clobazam gives the   S4  strongest signal of the analyzed benzodiazepines. In the same vein, seizures were observed in two clinical trials  (LundbeckLLC 2012 (LundbeckLLC , 2018)).
This implies that the liability to induce seizurecategory AEs is not uniform for benzodiazepine-type drugs. Comparing the rankings of the cumulative fraction of seizure/convulsion AEs within the drugs' AE reports with the fraction a drug contributes to the total pool of the AEs in the analyzed dataset, five drugs appear in both top 10 lists (see connections between upper and lower box plots in Fig. 4 ). Six of the compounds with seizure alerts are thought to exert their wanted effects chiefly by other targets, and their effects on GABA A Rs are considered an off-target effect, namely, phenytoin, lamotrigine, amoxapine, acamprosate, memantine, and olanzapine. The tricyclic compound amoxapine and the structurally related drug olanzapine, which were identified as potential orthosteric GABA A R antagonists using the NeuroDeRisk IL Profiler, both appeared in the top 30 compounds for our FAERS-based alert profile.
In addition, the ◂ GABA A agonist models did not retrieve any of the 81 drugs, and none were expected to have orthosteric agonist activity at GABA A Rs. In terms of seizure risk involving GABA A channel and orthosteric antagonist predictions, 27 drugs, including barbiturates and volatile anesthetics, hit at least one of the NeuroDeRisk IL Profiler GABA A seizure risk models. While the risk of seizure has been reported in the drug package inserts and pharmacovigilance FAERS alerts indicated the potential seizure risk of these drugs, the mechanisms of action for seizure induction are not clear. Therefore, the NeuroDeRisk IL Profiler is a useful tool for nominating drugs and investigational compounds for experimental studies to further investigate AEs and related GABA A R pharmacology, as demonstrated with the experimental followup on amoxapine in this study.
Related proteins and homology modeling potentially bridge the gap for binding sites of high scientific interest  (Bampali et al. 2022; Koniuszewski et al. 2022). To obtain more information about the seizure risk of the 81 drugs, pharmacovigilance data was utilized to search for seizure/convulsion AE alerts and to correlate with the findings from the molecular initiating event (ligand binding) screens. Pharmacovigilance data is generally considered to be a useful source for alerts and testable hypotheses, in spite of the inherent limitation  (Jeetu and Anusha 2010). Here, we used a dataset from the FAERS to analyze the associations of our test set of drugs with the AEs that can be reported on the basis of the MedDRA seizure/convulsion category. For drugs that were never approved in the USA, there are no or few records and thus no results (e.
g. , flutazolam, rilmafazone). For the majority of the tested compounds, a sufficient number of records is available and thus enables the use of appropriate statistical methods. This analysis not only results in an alert signal for amoxapine, but suggests that of the GABA A R targeting compounds that we analyzed, 43% are associated with seizure-category AEs and thus 57% are not, irrespective of ATC codes or chemotypes. The atypical benzodiazepine clobazam bears an ATC code as an anticonvulsant and yet appears to be firmly connected with seizure induction as shown in clinical trials  (LundbeckLLC 2012 (LundbeckLLC , 2018)). Amoxapine was "red-flagged" by the NeuroDeRisk IL Profiler and the FAERS analysis. Thus, preclinical assays that cover the molecular, cellular, tissue, and organism scales were employed to investigate its effects on GABA A R-mediated inhibitory transmission and on neuronal firing patterns.
Compound selection Small molecules which target GABA A Rs were selected from four publicly available sources (Drugbank (https:// go. drugb ank. com/), Wikipedia (https:// en. wikip edia. org/), Wikidata (WD; https:// www. wikid ata. org), and OpenTargets (https:// www. targe tvali dation. org/), without the intention to generate a complete list of all GABA A R targeting drugs, which would be beyond the scope. The selection was performed as follows: Data extraction of Drugbank data was performed with a python script that retrieves all drugs from each GABA A R subunit record, identified by the encoding gene name (example: https:// www. drugb ank. ca/ polyp eptid es/ P14867 for GABRA1). Likewise, for Open-Targets, all drugs associated with any of the 19 subunits were retrieved via the encoding gene names.
Analysis of protein sequence and structural data Sequences listed in the Supplementary Table  S7  were acquired from either UniProt or NCBI Protein databases. Next, MUSCLE  (Edgar 2004 ) was used to align the sequences per subunit isoform. Gap opening penalty was set to 2 and gap extension penalty to 0. 2. The alignments were imported into the Python v3. 9. 7 (Rossum and Drake 2009) environment by using the biopython v1. 78 package  (Cock et al. 2009). Next, all non-human sequences were compared to their human orthologs by using a custom substitution matrix. The matrix was constructed by taking the blosum90 substitution matrix. The values for insertions and deletions were set to -0. 5 and the value for a gap in both sequences to 0. The rest of the values were normalized linearly to a range between 0 and 1.
For the TMD, the four membranespanning domains were used. All the selected segments were converted into the corresponding alignment positions. The position of annotations for both parts of the sequences was corrected separately based on the generation of the alignment position number described above. Substitution score sequences and annotations were plotted using matplotlib  (Hunter 2007). All experimental structures have been taken from the protein data bank (www. rcsb. org). Superposition and analysis of pocket candidates in the entire cysloop family: PDBefold (http:// www. ebi. ac. uk/ msdsrv/ ssm/) and MOE (http:// www. chemc omp. com) were used to analyze structures. All molecular visualizations were done in MOE.