Role of sperm apoptosis and oxidative stress in male infertility: A narrative review

Activation of caspase, externalization of phosphatidyl serine, change in the mitochondrial membrane potential, and DNA fragmentation are apoptosis markers found in human ejaculated spermatozoa. Also, reactive oxygen species (ROS) play a vital role in the different types of male infertility. In this review, data sources including Google Scholar, Scopus, PubMed, and Science Direct were searched for publications with no particular time restriction to get a holistic and comprehensive view of the research. Apoptosis regulates the male germ cells, correct function and development from the early embryonic stages of gonadal differentiation to fertilization. In addition to maintaining a reasonable ratio between the Sertoli and germ cells, apoptosis is one of the well-known quality control mechanisms in the testis. Also, high ROS levels cause a heightened and dysregulated apoptotic response. Apoptosis is one of the well-known mechanisms of quality control in the testis. Nevertheless, increased apoptosis may have adverse effects on sperm production. Recent studies have shown that ROS and the consequent oxidative stress play a crucial role in apoptosis. This review aims to assimilate and summarize recent findings on the apoptosis in male reproduction and fertility. Also, this review discusses the update on the role of ROS in normal sperm function to guide future research in this area. Key words: Fertility, Spermatogonia, Apoptosis, Reproduction, DNA fragmentation, DNA integrity, ROS

The Effect of Deproteinized Bovine Bone Mineral on Saos-2 Cell Proliferation

Introduction: Deproteinized bovine bone mineral (Bio-Oss) is a xenogenic bone substitute, widely used in maxillofacial bone regeneration. The aim of this in vitro study was to investigate its influence on the growth behavior of human osteosarcoma cell line, Saos-2 culture, and compare it with the physiologic dose of Dexamethasone, an inductive factor for osteoblasts. Materials and Methods: Human osteosarcoma cells, Saos-2, were cultured on Bio-Oss and their growth rate was compared to Saos-2 cultures treated with Dexamethasone 10-7 M in contrast to cells cultivated in PBS, in the control group. Assessment of proliferation was performed after 24, 36, and 48 hours by counting cells using trypan blue exclusion method. Alkaline phosphatase was measured spectrophotometrically at 405 nm with paranitrophenol buffer. Results: After 48 hours, the number of Saos-2 cells increased significantly when subcultured with Bio-Oss. Bio-Oss was more effective on the enhancement of proliferation of Saos-2 cells when compared to the physiologic dose of Dexamethasone (P<0.05). Alkaline phosphatase activity increased in cells grown on Bio-Oss and dexamethasone 10-7M in contrast to cells cultivated in PBS control group. The greatest level of activity was observed in the group containing Bio-Oss after 48 hour. Conclusion: The significant increase of cell proliferation and alkaline phosphatase activity in cells cultured on Bio-Oss, compared to Dexamethasone-treated cells, suggests the important role of this bone substitute in promoting bone regeneration

Construction of a circRNA– lincRNA–lncRNA–miRNA–mRNA ceRNA regulatory network identifies genes and pathways linked to goat fertility

Background: There is growing interest in the genetic improvement of fertility traits in female goats. With high-throughput genotyping, single-cell RNA sequencing (scRNA-seq) is a powerful tool for measuring gene expression profiles. The primary objective was to investigate comparative transcriptome profiling of granulosa cells (GCs) of high- and low-fertility goats, using scRNA-seq.Methods: Thirty samples from Ji’ning Gray goats (n = 15 for high fertility and n = 15 for low fertility) were retrieved from publicly available scRNA-seq data. Functional enrichment analysis and a literature mining approach were applied to explore modules and hub genes related to fertility. Then, interactions between types of RNAs identified were predicted, and the ceRNA regulatory network was constructed by integrating these interactions with other gene regulatory networks (GRNs).Results and discussion: Comparative transcriptomics-related analyses identified 150 differentially expressed genes (DEGs) between high- and low-fertility groups, based on the fold change (≥5 and ≤−5) and false discovery rate (FDR <0.05). Among these genes, 80 were upregulated and 70 were downregulated. In addition, 81 mRNAs, 58 circRNAs, 8 lincRNAs, 19 lncRNAs, and 55 miRNAs were identified by literature mining. Furthermore, we identified 18 hub genes (SMAD1, SMAD2, SMAD3, SMAD4, TIMP1, ERBB2, BMP15, TGFB1, MAPK3, CTNNB1, BMPR2, AMHR2, TGFBR2, BMP4, ESR1, BMPR1B, AR, and TGFB2) involved in goat fertility. Identified biological networks and modules were mainly associated with ovary signature pathways. In addition, KEGG enrichment analysis identified regulating pluripotency of stem cells, cytokine–cytokine receptor interactions, ovarian steroidogenesis, oocyte meiosis, progesterone-mediated oocyte maturation, parathyroid and growth hormone synthesis, cortisol synthesis and secretion, and signaling pathways for prolactin, TGF-beta, Hippo, MAPK, PI3K-Akt, and FoxO. Functional annotation of identified DEGs implicated important biological pathways. These findings provided insights into the genetic basis of fertility in female goats and are an impetus to elucidate molecular ceRNA regulatory networks and functions of DEGs underlying ovarian follicular development

Deep Q‐learning recommender algorithm with update policy for a real steam turbine system

Abstract In modern industrial systems, diagnosing faults in time and using the best methods becomes increasingly crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning (DL) have proposed various methods for data‐based fault diagnosis, and the authors are looking for the most reliable and practical ones. A framework based on DL and reinforcement learning (RL) is developed for fault detection. The authors have utilised two algorithms in their work: Q‐Learning and Soft Q‐Learning. Reinforcement learning frameworks frequently include efficient algorithms for policy updates, including Q‐learning. These algorithms optimise the policy based on the predictions and rewards, resulting in more efficient updates and quicker convergence. The authors can increase accuracy, overcome data imbalance, and better predict future defects by updating the RL policy when new data is received. By applying their method, an increase of 3%–4% in all evaluation metrics by updating policy, an improvement in prediction speed, and an increase of 3%–6% in all evaluation metrics compared to a typical backpropagation multi‐layer neural network prediction with comparable parameters is observed. In addition, the Soft Q‐learning algorithm yields better outcomes compared to Q‐learning

Effect of sevelamer on serum phosphorus levels in chronic kidney disease and hemodialysis patients; a systematic review and meta-analysis

Introduction: Hyperphosphatemia is an independent risk factor for mortality in chronic kidney disease (CKD) patients. Objectives: This systematic review and meta-analysis aimed to investigate the effect of Sevelamer on serum phosphorus levels in CKD and hemodialysis patients. Materials and Methods: The data were obtained after searching the international databases of Cochrane, PubMed, Scopus, Web of Science, and the Google Scholar search engine until February 28, 2023. The heterogeneity of articles was assessed using the I2 index. The data were analyzed in STATA 14, and P values < 0.05 were considered significant. Findings: A total of 22 articles were assessed with a total sample size of 3221. Sevelamer reduced calcium levels in CKD and hemodialysis patients compared with those in the comparison group (standardized mean difference [SMD]: -0.67; 95% CI: -1.23, -0.11); however, sevelamer had no significant effect on serum parathyroid hormone (PTH) levels (SMD: 0.07; 95% CI: -0.39, 0.54) and Ca × P product (SMD: -0.20; 95% CI: -0.41, 0). A significant decrease in serum phosphorus level was observed in patients who had taken sevelamer for a maximum of 12 weeks compared with the comparison group (SMD: -0.27; 95% CI: -0.54, -0.01); however, no significant decrease in serum phosphorus level was observed in patients who had taken sevelamer for more than 12 weeks. A significant decrease in serum phosphorus level was observed in sevelamer users compared to placebo group members (SMD: -0.36; 95% CI: -0.68, -0.05). Conclusion: The administration of sevelamer reduced serum phosphorus levels in CKD and hemodialysis patients compared with those in the placebo group in the short term. Therefore, physicians are recommended to prescribe sevelamer for a maximum period of three months. Registration: This study has been compiled based on the PRISMA checklist, and its protocol was registered on the PROSPERO website (ID: CRD42023406804)

A methodological checklist for fMRI drug cue reactivity studies:development and expert consensus

Cue reactivity measured by functional magnetic resonance imaging is used in studies of substance-use disorders. This Consensus Statement is the result of a Delphi process to arrive at parameters that should be reported in describing these studies. Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: Participants Characteristics, General fMRI Information, General Task Information, Cue Information, Craving Assessment Inside Scanner, Craving Assessment Outside Scanner and Pre- and Post-Scanning Considerations. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the General fMRI Information category were reported in 90.5% of the reviewed papers, items in the Pre- and Post-Scanning Considerations category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.Funding Agencies|National Institute on Alcohol Abuse and Alcoholism (NIAAA)United States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Alcohol Abuse &amp; Alcoholism (NIAAA) [P50 AA010761]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)German Research Foundation (DFG) [402170461-TRR 265, 40217046-TRR 265, 421888313, 437718741, 324164820]; DFGGerman Research Foundation (DFG)European Commission [402170461 - TRR 265]; California Tobacco-Related Disease Research Grant Program of the University of California [T30IP0962]; Laureate Institute for Brain Research (LIBR); Warren K. Family Foundation; Oklahoma Center for Advancement of Science and Technologies (OCAST) [HR18-139]; Brain and Behavior Foundation (NARSAD Young Investigator Award) [27305]; National Institute on Drug Abuse (NIDA)United States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Drug Abuse (NIDA) [R01 DA030344, R21DA044465]; NIDAUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Drug Abuse (NIDA) [R01DA041528, R01DA048301, R01DA047851, K23DA042898, K12 DA000167, U01 DA041089, U24 DA041147, P30 DA048742]; NCCIH [R01AT010627]; NIAAAUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Alcohol Abuse &amp; Alcoholism (NIAAA) [F32AA027699, R01 AA027765, R01 AA026859, U01 AA021692, U24 AA021695, R01AA023665, R01AA022328]; NIHUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USA [R01DA039135, K02DA042987, R01 DA041866, R01AA026844, K08AA023545, K23AA023894, R01DA040670, R21HL144673, R01DA041438, R21DA045853]; Bundesministerium fur Bildung und ForschungFederal Ministry of Education &amp; Research (BMBF) [FKZ: 01ZX1503, 01ZX1909B]; Shanghai Municipal Science and Technology Major project [2019SHZDZX02]; Eli Lilly Canada Chair on schizophrenia researchEli Lilly; Australian Medical Research Future FundMedical Research Future Fund (MRFF) [MRF1141214]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81871426]</p

A methodological checklist for fMRI drug cue reactivity studies : development and expert consensus

Cue reactivity measured by functional magnetic resonance imaging is used in studies of substance-use disorders. This Consensus Statement is the result of a Delphi process to arrive at parameters that should be reported in describing these studies. Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: Participants Characteristics, General fMRI Information, General Task Information, Cue Information, Craving Assessment Inside Scanner, Craving Assessment Outside Scanner and Pre- and Post-Scanning Considerations. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the General fMRI Information category were reported in 90.5% of the reviewed papers, items in the Pre- and Post-Scanning Considerations category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.Funding Agencies|National Institute on Alcohol Abuse and Alcoholism (NIAAA)United States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Alcohol Abuse &amp; Alcoholism (NIAAA) [P50 AA010761]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)German Research Foundation (DFG) [402170461-TRR 265, 40217046-TRR 265, 421888313, 437718741, 324164820]; DFGGerman Research Foundation (DFG)European Commission [402170461 - TRR 265]; California Tobacco-Related Disease Research Grant Program of the University of California [T30IP0962]; Laureate Institute for Brain Research (LIBR); Warren K. Family Foundation; Oklahoma Center for Advancement of Science and Technologies (OCAST) [HR18-139]; Brain and Behavior Foundation (NARSAD Young Investigator Award) [27305]; National Institute on Drug Abuse (NIDA)United States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Drug Abuse (NIDA) [R01 DA030344, R21DA044465]; NIDAUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Drug Abuse (NIDA) [R01DA041528, R01DA048301, R01DA047851, K23DA042898, K12 DA000167, U01 DA041089, U24 DA041147, P30 DA048742]; NCCIH [R01AT010627]; NIAAAUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Alcohol Abuse &amp; Alcoholism (NIAAA) [F32AA027699, R01 AA027765, R01 AA026859, U01 AA021692, U24 AA021695, R01AA023665, R01AA022328]; NIHUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USA [R01DA039135, K02DA042987, R01 DA041866, R01AA026844, K08AA023545, K23AA023894, R01DA040670, R21HL144673, R01DA041438, R21DA045853]; Bundesministerium fur Bildung und ForschungFederal Ministry of Education &amp; Research (BMBF) [FKZ: 01ZX1503, 01ZX1909B]; Shanghai Municipal Science and Technology Major project [2019SHZDZX02]; Eli Lilly Canada Chair on schizophrenia researchEli Lilly; Australian Medical Research Future FundMedical Research Future Fund (MRFF) [MRF1141214]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81871426]</p