dataesr/scientific-paragraphs-categorization huggingface dataset:
The regulatory mechanisms of CLDN-5 by junctional proteins and associated signaling in the brain ECs CLDN5, the down-stream gene of junctional proteins CLDN5 was originally identified as one of the deleted genes in 22q11 deletion syndrome (22q11DS), also known as DiGeorge syndrome or Velocardiofacial syndrome, and was named TMVCF (transmembrane protein deleted in velocardiofacial syndrome)  [24]. In humans, 4 mRNA transcripts are registered in the NCBI database; transcript variant 4 encodes a shorter CLDN-5 protein (isoform 2) and the other transcriptional variants encode an N-terminally extended longer CLDN-5 protein (isoform 1) (Fig. 2 ). The major transcription initiation site is located between the start codon of isoforms 1 and 2 and variant 4 is the predominant mRNA variant. Although a prediction tool suggests that the start codon of the longer isoform encoding transcriptional variants is functional [47] ZONAB  [121]  β-catenin/FoxO1/Tcf4/Suz12/Ezh2/Eed  [57]  Eed, embryonic ectoderm development; Ezh2, enhancer of zeste homolog 2; FoxO1, forkhead box protein O1; Suz12, suppressor of zeste 12; Tcf4, transcription factor 4; ZONAB, ZO-1-associated nucleic acid binding protein  [25] , the longer isoform of CLDN-5 has not been detected by Western blotting.
Funding This work was supported by grants from  Science Foundation Ireland (SFI) , ( SFI/ TCD 21/SPP/3732 ), The  Irish Research Council (IRC)  and by a research grant from  SFI  under grant number  16/RC/3948  and co-funded under the European Regional Development fund by FutureNeuro industry partners. The  Campbell laboratory  is also supported by a  European Research Council (ERC)  grant, ' Retina-Rhythm ' (864522).
Participants Eleven women from the French U20 national ice hockey team were included in this study. Participants' characteristics are presented in Table  1. During the total duration of the study, the physical load was standardized for all participants. None reported injuries within the last three months or any clinical contraindication that could affect data collection or experimental results. Prior to participation, they were fully informed about the purpose of the study and the experimental procedure. However, participants were blind to our a priori hypothesis. All signed an informed consent form (signed by their parents for those athletes under 18 years old). This study was conducted according to the declaration of Helsinki. Approval was obtained from the CERSTAPS (ethics committee for research in sports sciences; IRB00012476-2022-15-03-165).
The sample size was calculated a priori using G*Power (version 3. 1. 9. 6, free software available at https://www. psychologie. hhu. de/ arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower. html, accessed Sports 2023, 11, 108 3 of 10 on 10 October 2019) with the following parameters: effect size of 0. 15 (partial-eta-squared, ηp2), power of 0. 8, probability error of 0. 05. A sample size of 11 individuals was indicated.
The tests included an arrowhead agility test, a 20 m sprint, a repeated sprint ability test (RSA), a simple reaction time task, and a NASA task load index questionary (NASA-TLX). Sports 2023, 11, 108 priori using G*Power (version 3. 1. 9. 6, free software available at https://www. psy gie. hhu. de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower. html accessed on 10 October 2019) with the following parameters: effe of 0. 15 (partial-eta-squared, ηp2), power of 0. 8, probability error of 0. 05. A sample s 11 individuals was indicated.
5, 0. 5-1. 2, and 1. 2 representing small, medium, and large magnitudes of change, respectively  [22]. p < 0. 05 was taken as the level of statistical significance for all comparisons. Absolute values are expressed as mean ± SD or mean difference with 95% confidence intervals (95%CI). Statistics were performed using the JASP Software (version 0. 13, JASP Team (2020), University of Amsterdam).
The data were collected in public spaces in cities selected during the quantitative survey phase. Two senior sociologists (EB, FL) collected the data. Recruitment was allowed to continue until thematic saturation was achieved, i. e. , until no new major themes were identified in the data  (12). Responses were recorded using a digital recorder. The device was completed by live notetaking, with particular focus on non-verbal language. One interviewer took notes while the other asked questions and recorded the interview. Each interview was audio-recorded anonymously in addition to the notes taken by the researchers.
Methods This is an ancillary and retrospective analysis of data from patients included in a prospective single-center study (ethic agreement: CPP-66/17) investigating the impact of PEEP variations and prone position on ventilation and perfusion distributions  [4]. Recordings made with a PEEP level of 12 cmH 2 O in the supine position were selected for this study. Patients were intubated, sedated, paralyzed, ventilated in volume control mode with a tidal volume between 6 and 8 mL/kg of predicted body weight (PBW). Ventilation and perfusion distributions were studied using the Enlight 1800 (TIMPEL SA, São Paulo, Brazil). Cardiac output (Q C ) was measured using the Vigileo device (Edwards Lifesciences, Irvine, CA), in hemodynamically stabilized patients. Alveolar ventilation (V A ) was calculated as 70% of the expiratory minute ventilation measured on the mechanical ventilator (in order to remove ventilation of anatomical and instrumental dead space).
Eligibility criteria {10} The authors will enrol 130 patients undergoing difficult CTO-PCI. The inclusion criteria will be: 1 Men and women aged 18 to 90 years with body mass index (BMI) ≤ 40 kg/m 2 ; 2 Coronary artery chronic total occlusion (TIMI flow 0 for > 3 months) associated with myocardial viability (intended like normokinetic/hypokinetic wall motion or instrumental data in favour of viability) associated with ≥ 2 Canadian Cardiovascular Society (CCS) angina or ≥ 2 New York Heart Association (NYHA) dyspnoea or documented ischemia or ejection fraction < 50%; 3 Coronary angiography J-CTO score ≥ 2; 4 Understanding of, and agreement to comply with the study procedures; 5 Signature of informed consent form prior to any study-related procedures; and 6 Affiliation to social security scheme.
Sequence generation {16a} An unstratified randomisation sequence will be generated before the commencement of the study by a software algorithm (IWRS, ClinInfo SAS, Lyon, France) and to be used in assigning patients to the CT group (A) or angiography group (B) in a 1:1 ratio. The randomisation list will consist of fixed block sizes of 4, e. g. AABB-BAAB-BABA-ABBA.
Concealment mechanism {16b} The randomisation list generated in the IWRS software will be concealed from staff as well as patients. Only once the patient is included in the trial, will the treatment arm be assigned to the patient by the software, and reflected in the patient's electronic case report form (eCRF) which is only accessible by the study staff. Patients will be blinded to their allocated group up to the point that they receive the treatment-patients will be aware of whether they receive CCTA before the CTO PCI.
Plans for assessment and collection of outcomes {18a} The following datasets will be defined: 1. Intention-to-treat (effect of assigning the treatment): This dataset will include all enrolled patients allocated to either the CT group or the angiography group, irrespective of whether they undergo CTO-PCI, cross-over between treatment arms, or have incomplete post-intervention data. 2. Per Protocol (effect of receiving the treatment): This dataset will include enrolled patients allocated to either the CT group or the angiography group, who undergo CTO-PCI, remain in their allocated treat-ment arm, and have complete post-intervention data (TIMI flow and residual stenosis available after PCI).
As-treated (effect of adherence to a treatment arm): This dataset will include enrolled patients allocated to either the CT group or the angiography group, who undergo CTO-PCI, irrespective of whether they cross-over between treatment arms, and who have complete post-intervention data (TIMI flow and residual stenosis available after PCI). The following information will be included on the eCRF after enrolment and allocation: 1. Patients' characteristics: age (years), sex, weight (kg), height (cm), and pregnancy test. 2. Risk factors: smoking status, diabetes, hyperlipidaemia, hypertension, cardiovascular disease family history 3. Symptoms related to the pathology (angina, dyspnea) 4. Clinical/instrumental data: ECG, angiographic ejection fraction (EF), regional wall motion, ischemia, viability.
5. Laboratory data: Troponin, creatine kinase, creatinine, clearance of creatinine, haemoglobin. 6. Anamnestic data: previous PCI, previous CTO-PCI attempt without success, previous bypass surgery. 7. Relevant comorbidity (renal function disease, pulmonary disease, peripheral artery disease, others), arrhythmias, allergy 8. Coronary anatomy based on coronary angiography and to calculate the J-CTO score for all the patients. For the CT group, coronary anatomy will be reassessed with the CCTA and in addition to the J-CTO Score based on the CCTA, the CT-RECTOR and calcium scores will be calculated. The primary endpoint, that is the success rate of difficult CTO-PCI in ≤60 min wire manipulation, will be assessed for each of the three datasets, i. e. intention-totreat, per-protocol and as-treated, after the CTO-PCI (D0, Table  2 ).
The TIMI flow and residual stenosis after the procedure will be recorded for each of the three datasets, i. e. intention-to-treat, per-protocol and as-treated, and will be assessed at immediate follow-up (D0 + 2 to 4 days) and final follow-up (D0 + 180 days). Laboratory parameters will be recorded for each of the three datasets, i. e. intention-to-treat, per-protocol and as-treated, and will be assessed at immediate follow-up (D0 + 2 to 4 days). The success rate of the complex CTO-PCI between both groups will be calculated for each of the three datasets, i. e. intention-to-treat, per-protocol and as-treated, at immediate follow-up CTO-PCI (D0 + 2 to 4 days) and final follow-up (D0 + 180 days). For all three datasets, i. e. Intention-to-treat, Per-protocol and As-treated, all adverse events will be recorded according to the corresponding system organ class (SOC) and preferred term (PT) using the MedDRA terminology.
Plans to promote participant retention and complete follow-up {18b} The data for primary and secondary endpoints will be collected during CTO-PCI, and the risk of missing data is therefore small (5% max). If a patient withdraws prematurely from the study they will not be replaced, and reasons for patient withdrawal must be documented in the eCRF. For patients lost to follow-up, attempts to contact the patient must be made and documented in the patient's eCRF.
Data management {19} Data management will be completed using good clinical practices. All data will be entered, and a manual review will be done to avoid data entry error. The data will be entered into a validated database. The main author will be responsible for data processing, in accordance with the sponsor data management procedures. Validation checks will be performed and documented to ensure accurate, consistent, and reliable data. Data identified as erroneous, or data that are missing, will be referred to the investigative site for resolution through data queries. Correction procedures will be tracked. The database will be locked once quality assurance procedures have been completed prior to statistical analysis. For medical information, the following dictionaries will be used:.
Confidentiality {27} In accordance with the legal provisions in force (Articles L. 1121-3 and R5121. 13 of the Public Health Code), persons having direct access to the source data will take all necessary precautions to ensure the confidentiality of information related to experimental drugs, research, persons included in the study and especially their personal identity and the research results. Those persons, as well as the investigators themselves, are subject to professional secrecy. Each enrolled patient will have a personal eCRF with a unique identifier, in which all data related to the study will be recorded. Data collection will be the responsibility of the clinical trial staff at the site under the supervision of the site investigator. The investigator is responsible for ensuring the accuracy, completeness, legibility, and timeliness of the data entry.
All source documents should be completed in a neat, legible manner to ensure accurate interpretation of data. All information required for the protocol has to be entered in the eCRF, and the investigator has to provide justification in cases of missing data entries. During the biomedical research or at the end of it, the data collected on the persons who are suitable and transmitted to the main author by the investigators will be anonymised, using the unique identifier of the case report form, to hide their name, address, contact information and date of birth. The trial steering committee will ensure that each enrolled patient provided written informed consent for access and use of their anonymised data.
Plans to give access to the full protocol, participant-level data and statistical code {31c} The research documents concerned by the biomedical research regulations have to be archived by all parties for a 15-year period after completion of the study, in accordance with the Good Clinical Practice guidelines. This archiving will have at least: 1. The protocol and the potential amendments, 2. All eCRFs, 3. All signed informed consent forms with the list or inclusion register in correspondence, 4. The letters of correspondence with the trial steering committee, 5. All specific appendix, 6. The final report of the study from the statistical analysis and the study quality control, 7. The database used for the statistical analysis, and 8. The audit certificates done during the research.
The 'cause of death' section comprises of two parts: in part one, the sequence of morbid conditions is reported in descending order from the immediate to the underlying causes of death (4 lines); in part two, unrelated but contributory causes of death are reported (2 lines). Each part consists of free-text fields where physicians write down one or more causes of death using one or more words. The French Epidemiology Centre on Medical Causes of Death (Inserm-CépiDc) is responsible for the French national causes of death database. It implements the coding process following WHO rules, from the Tenth revision of the International Classification of Diseases (ICD-10)  (World Health Organization, 2016). It can take between 4 and 6 months for the free-text medical causes of death (FT-MCD) to be made available to epidemiologists and even longer for the subsequent coding.
Data All death certificates for the period 2015 to March 2022 in France were available. For the period 2015 to 2017, for each certificate, medical causes of deaths were coded using ICD-10 codes and an underlying cause of death was identified. FT-MCD written by medical practitioners was also available for all certificates. For the period 2018 to March 2022, only FT-MCD written by medical practitioners were analysed in the present study, since ICD-10 coding was still ongoing when the study started. Furthermore, the number of deaths on this period is still provisional, as a very limited part of paper death certificates is usually missing (less than 2% of total number of deaths). The use of mortality data in the frame of public health surveillance has been authorized by the French National Commission for Data protection and Liberties (CNIL).
e. , coded) ICD-10 code. Based on this initial list of expressions, the algorithm was applied to identify suicide mortality data for 2016. The performance of the algorithm in terms of sensitivity and predictive positive value was improved iteratively by adding or removing missing terms or expressions to the algorithm, based on an analysis of non-classified or misclassified death certificates (i. e. , identified with an ICD-10 code but not with the algorithm or vice-versa, respectively), compared with the gold-standard. The final performance analysis was then conducted on data for the 2015 to 2017 period. Sensitivity, predictive positive value and F-measure (harmonic mean of the two previous measures) were calculated for each year for paper and electronic certificates, which were considered separately in order to check that the text mentioned was comparable between the two sources and then together.
, 2021). The individual information declared on death certificates about the mental history and healthcare of the patient prior to death are too limited to be able to provide robust analysis of the characteristics of patients who died. In particular, a physician who declares a suicide is not systematically the referring physician of the deceased person and does not know his possible psychiatric conditions. Complementary studies using data from the National Hospitalization Database (PMSI) and about the reimbursements of medical care could be conducted in order to better explore the health status of suicidants during the pandemic in comparison with a prior reference period, following the design of a previous study  (Laanani et al. , 2020). Beyond the indirect impacts of the COVID-19 epidemic, the mental health consequences and the increase in suicide mortality may be continued in the future, with the international context related to the war in Ukraine.
Acknowledgements. The authors thank all physicians who fill in the death certificates, particularly those using the electronic certification system. The authors also thank the organizations (national and regional public health decision-makers, the  IT department and CépiDc in Inserm ) who contributed to maintain a reactive data collection during this COVID-19 crisis. They also thank  Jude Sweeney  for his support on the English editing of the manuscript. Financial Support. This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Metformin and paclitaxel therapy offer promising outcomes in the treatment of liver cancer. Combining paclitaxel with metformin enhances treatment effectiveness and mitigates the adverse effects associated with paclitaxel alone. This study explored the anticancer properties of metformin and paclitaxel in HepG2 liver cancer cells, MCF-7 breast cancer cells, and HCT116 colon cancer cells. The results demonstrated that the combination of these agents exhibited a lower IC50 in the tested cell lines compared to paclitaxel monotherapy. Notably, treating the HepG2 cell line with this combination led to a reduction in the G0/G1 phase and an increase in the S and G2/M phases, ultimately triggering early apoptosis. To further investigate the interaction between the cellular proteins with paclitaxel and metformin, an in silico study was conducted using proteins chosen from a protein data bank (PDB).
Molecular Docking Study: Ligand and Protein Determination and Preparation The computational experiments were conducted using a molecular docking server (https://www. dockingserver. com accessed on 13 November 2021 (r306)) that is based on AutoDock 4 for docking calculation  [14]. The partial charges of the protein and ligand were calculated using the PM6 technique using the MOPAC2009 program  [15]. The necessary hydrogen atoms and solvation parameters were added using AutoDock tools. The van der Waals and electrostatic energies were calculated using distance-dependent dielectric functions. Docking simulations were performed using the Solis and Wets methods, the Lamarckian genetic algorithm, and other methods. Each docking test was explained using data from ten independent runs, each of which was designed to end after receiving 250,000 maximum energy evaluations.
Using the PubChem database, the three-dimensional (3D) chemical structures of the selected molecules of proteins interacting with MET and PXT were retrieved. The 3D and geometry improvements with ligand energy reduction were performed using algorithms controlled by the docking server. The built-in Merck Molecular Force Field94 (MMFF94) was utilized for the geometry optimization strategy for the ligand synthesis module, and the charge calculation method Gasteiger was applied at pH 7. AMPK, autophagy, and apoptotic proteins were picked from the RCSB Protein Data Bank (http://www. rscb. org accessed on 5 July 2023). The molecules that medications were made to block were protein kinases, specifically AMPK core with ATP (PDB: 4EAG), EGFRK (PDB: 1M17), and FKBP12-rapamycin-mTOR-FRB (PDB: 1FAB), the PPIase domain of FKBP51-mTOR-FRB (PDB: 4DRH).
Other important autophagy proteins included Beclin 1 (PDB: 3Q8T), LC3 (structure of the WD40 domain of human ATG16L1), (PDB: 5NUV), and ATG4A (PBB 2P82) as autophagic proteins. In addition to apoptotic genes, TP53 (PDB: 3DCY) and caspase-3 (PDB: 3GJQ) were included. The protein structures were generated using the protein preparation wizard in the molecular docking server panel. Bond ordering was formed, and hydrogen atoms were also introduced. Water molecules and other unimportant substances were eliminated. Affinity (grid) maps with a 202,020 grid and 0. 375 spacing were made using the Auto grid application  [16]. The AutoDock parameter set-and distance-dependent dielectric functions were used to determine the van der Waals and electrostatic components, respectively. To simulate docking, the Solis and Wets local search method and the Lamarckian genetic algorithm (LGA) were also employed  [17].
Real-Time qPCR with SYBR Green Real-time quantitative polymerase chain reaction with SYBR Green was used to measure gene mRNA expression in treated HepG-2 cells, with GAPDH serving as an internal reference (PCR). A SYBR Green/ROX qPCR Master (Thermo Scientific, USA, # K0221) and gene primers were used. Adenosine Monophosphate Kinase (AMPK-α), Epidermal Growth Factor (EGFR), Total P53 (Tp53), Caspase3, Beclin1, and ATG4A primers are shown in Table  1. The primers were chosen using the website (http://www-genome. wi. mit. edu/cgi-bin/primer/primer3www. cgi website of the Whitehead Institute for Biomedical research that accessed on 13 October 2022) and double-checked and validated as unique via similarity checking using the Basic Local Alignment Search Tool (BLAST) (www. ncbi. nlm. nih. gov/blast/Blast.
Statistical Analysis The viability of treated cells is given as a mean ± standard deviation (SD). By multiplying the absorbance of treated cells by 100 and dividing it by the absorbance of untreated cells, the percentage of viability was estimated. GraphPad Prism Software (version 9. 0, San Diego, CA, USA) was used to calculate mean and SD and compute the drug IC 50. The flow cytometry software from Applied Biosystems automatically calculated the proportion of cells in each phase of the cell cycle as well as the percentage of cells of necrotic and apoptotic cells.
Metformin and Paclitaxel Bind to Related Protein Signaling Pathway AMPK modulation (downstream and/or upstream regulatory proteins of cell signaling pathways involved in cell survival and proliferation) is the biological mechanism by which PXT and MET modulate the activity of apoptosis and autophagy. The complexes were docked, and the PLIP web server was used to evaluate them. The possible docking modes of protein-ligand interaction were calculated and displayed by the molecular docking server based on AutoDock 4. This enables a molecular understanding of the interaction between PXT and MET 3D structures imported from the PubChem database and the crystal structures of protein kinases (AMPK, EGFRK (PDB ID: 4EAG), 1M17); autophagy-related proteins such as FKBP12-rapamycin-mTOR-FRB, the PPIase domain of FKBP51-mTOR-FRB, Beclin 1, and ATG16L1 ATG4A (PDB ID:1FAP, 4DRH, 3Q8T, 5NUV, 2P82); and apoptosis-related proteins (TP53, Caspase3, (PDB ID: 3DCY, 3GJQ)), which were imported from the RCSB protein data bank.