Multiple morphological abnormalities of the flagella (MMAF) represent a severe form of sperm defects leading to asthenozoospermia and male infertility. In this study, we identified a novel homozygous splicing mutation (c.871-4 ACA>A) in the adenylate kinase 7 (AK7) gene by whole-exome sequencing in infertile individuals. Spermatozoa from affected individuals exhibited typical MMAF characteristics, including coiled, bent, short, absent, and irregular flagella. Transmission electron microscopy analysis showed disorganized axonemal structure and abnormal mitochondrial sheets in sperm flagella. Immunofluorescence staining confirmed the absence of AK7 protein from the patients' spermatozoa, validating the pathogenic nature of the mutation. This study provides direct evidence linking the AK7 gene to MMAF-associated asthenozoospermia in humans, expanding the mutational spectrum of AK7 and enhancing our understanding of the genetic basis of male infertility.
Humans ; Male ; Sperm Tail/ultrastructure* ; Homozygote ; Consanguinity ; Asthenozoospermia/pathology* ; Infertility, Male/genetics* ; Mutation ; Pakistan ; Adenylate Kinase/genetics* ; Adult ; Pedigree ; RNA Splicing ; Exome Sequencing ; Spermatozoa

Colorectal cancer has substantial morbidity and mortality. Approximately one-quarter of cases are overlooked during screening colonoscopy, leading to interval colorectal cancer. The use of artificial intelligence (AI) through deep learning systems has demonstrated promising results in the detection of polyps and adenomas. Consequently, our objective was to evaluate the impact of AI on adenoma detection. To identify relevant studies, we searched the PubMed, MEDLINE, and Cochrane Library databases without restrictions on publication date. Ultimately, we analyzed 16 randomized controlled trials involving 13,685 participants. The primary outcome assessed was the effect of AI-assisted colonoscopy (AIAC) on the adenoma detection rate (ADR). Secondary outcomes included the polyp detection rate (PDR) and adenomas per colonoscopy (APC). A random-effects model was used to calculate pooled effect sizes, and statistical heterogeneity was evaluated using the Higgins I 2 statistic, with I 2 cutoff points of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. Publication bias was investigated using a funnel plot, and the quality of evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation framework. The findings indicated a 26% greater ADR with AIAC than with standard colonoscopy (40.4% vs. 31.9%). Additionally, AIAC was associated with a 30% greater PDR (52.9% vs.40.1%) and a 44% higher APC. The findings demonstrate that the integration of AI in colonoscopy improves ADR, PDR, and APC, potentially reducing the incidence of interval colorectal cancer.

Colorectal cancer has substantial morbidity and mortality. Approximately one-quarter of cases are overlooked during screening colonoscopy, leading to interval colorectal cancer. The use of artificial intelligence (AI) through deep learning systems has demonstrated promising results in the detection of polyps and adenomas. Consequently, our objective was to evaluate the impact of AI on adenoma detection. To identify relevant studies, we searched the PubMed, MEDLINE, and Cochrane Library databases without restrictions on publication date. Ultimately, we analyzed 16 randomized controlled trials involving 13,685 participants. The primary outcome assessed was the effect of AI-assisted colonoscopy (AIAC) on the adenoma detection rate (ADR). Secondary outcomes included the polyp detection rate (PDR) and adenomas per colonoscopy (APC). A random-effects model was used to calculate pooled effect sizes, and statistical heterogeneity was evaluated using the Higgins I 2 statistic, with I 2 cutoff points of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. Publication bias was investigated using a funnel plot, and the quality of evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation framework. The findings indicated a 26% greater ADR with AIAC than with standard colonoscopy (40.4% vs. 31.9%). Additionally, AIAC was associated with a 30% greater PDR (52.9% vs.40.1%) and a 44% higher APC. The findings demonstrate that the integration of AI in colonoscopy improves ADR, PDR, and APC, potentially reducing the incidence of interval colorectal cancer.

Purpose: Proctitis distal to colorectal anastomosis is rare and infrequently reported. We evaluated the incidence, symptoms, treatment, and potential risk factors associated with this condition. Methods: We conducted a retrospective population-based cohort study in Västmanland County, Sweden. This investigation included all patients who underwent sigmoidectomy with colorectal anastomosis between 2008 and 2020. We excluded patients without an anastomosis and those with inflammatory bowel disease. Results: Of the 546 patients identified, 233 fulfilled the inclusion criteria, of whom 26 (11.2%) developed proctitis distal to colorectal anastomosis. The most frequent symptoms included urgency (n=16, 61.5%), increased stool frequency (n=12, 46.2%), and anorectal pain (n=12, 46.2%). Endoscopic balloon dilation was performed in 20 cases (76.9%), with 10 requiring only a single therapeutic procedure. The median number of dilations was 3 (range, 1–8). Multivariable analysis revealed that surgery due to malignancy and emergency surgery were associated with elevated risk of proctitis. A subgroup analysis of patients who underwent surgery due to malignancy indicated that smoking (odds ratio, 3.9; 95% confidence interval, 1.1–14.0) and emergency surgery (odds ratio, 6.5; 95% confidence interval, 1.1–37.1) were also associated with increased proctitis risk. Conclusion Proctitis distal to colorectal anastomosis is not uncommon following sigmoidectomy. Patients undergoing emergency surgery or surgery due to malignancy and who had a history of smoking displayed an increased risk of developing proctitis. Due to the paucity of symptoms observed, particularly in patients with a diverting stoma, routine endoscopic rectal examination should be performed during follow-up after sigmoidectomy.

Colorectal cancer has substantial morbidity and mortality. Approximately one-quarter of cases are overlooked during screening colonoscopy, leading to interval colorectal cancer. The use of artificial intelligence (AI) through deep learning systems has demonstrated promising results in the detection of polyps and adenomas. Consequently, our objective was to evaluate the impact of AI on adenoma detection. To identify relevant studies, we searched the PubMed, MEDLINE, and Cochrane Library databases without restrictions on publication date. Ultimately, we analyzed 16 randomized controlled trials involving 13,685 participants. The primary outcome assessed was the effect of AI-assisted colonoscopy (AIAC) on the adenoma detection rate (ADR). Secondary outcomes included the polyp detection rate (PDR) and adenomas per colonoscopy (APC). A random-effects model was used to calculate pooled effect sizes, and statistical heterogeneity was evaluated using the Higgins I 2 statistic, with I 2 cutoff points of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively. Publication bias was investigated using a funnel plot, and the quality of evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation framework. The findings indicated a 26% greater ADR with AIAC than with standard colonoscopy (40.4% vs. 31.9%). Additionally, AIAC was associated with a 30% greater PDR (52.9% vs.40.1%) and a 44% higher APC. The findings demonstrate that the integration of AI in colonoscopy improves ADR, PDR, and APC, potentially reducing the incidence of interval colorectal cancer.

BACKGROUND: Stress cardiomyopathy (SCM) currently has a high incidence in older adults, and the theories regarding its causes include "catecholamine myocardial toxicity" and "sympathetic hyperactivation". However, the role of the central nervous system in the pathogenesis of SCM remains unknown. We investigated the role of microglia activation in the paraventricular hypothalamic nucleus (PVN) in the development of SCM. METHODS: An SCM model was created using male Sprague-Dawley (SD) rats, immobilized for 6 h every day for a week. Electrocardiogram, cardiac electrophysiology, and echocardiography examinations were performed to verify the changes in cardiac structure and function in rats with SCM. RNA sequencing was used to explore the changes in the hypothalamus during SCM. In addition, brain and heart tissues were collected to detect microglial activation and sympathetic activity. RESULTS: The main findings were as follows: (1) immobilization stress successfully induced SCM in SD rats; (2) microglia were significantly activated in the hypothalamus, as evidenced by cytosol thickening, increases in the number of microglial branches, and microglia enriched in the PVN; (3) in SCM, the microglia in the PVN exhibited increased central and peripheral cardiac sympathetic activity and increased the expression of neuroinflammatory factors; and (4) it is possible that inhibiting microglial activation could suppress the sympathetic activity of the central nervous system and heart and increase cardiac electrical stability in SCM rats. CONCLUSIONS SCM was induced in SD rats by immobilization stress, acting through the activation of the hypothalamic microglia. The activated microglia were specifically enriched in the PVN, increasing the activity of the central and peripheral sympathetic nervous systems by regulating the expression of neuro-inflammatory factors, mediating dysfunction of the left ventricle, and increasing the susceptibility to ventricular arrhythmias.

Asthenoteratozoospermia is one of the most severe types of qualitative sperm defects. Most cases are due to mutations in genes encoding the components of sperm flagella, which have an ultrastructure similar to that of motile cilia. Coiled-coil domain containing 103 (CCDC103) is an outer dynein arm assembly factor, and pathogenic variants of CCDC103 cause primary ciliary dyskinesia (PCD). However, whether CCDC103 pathogenic variants cause severe asthenoteratozoospermia has yet to be determined. Whole-exome sequencing (WES) was performed for two individuals with nonsyndromic asthenoteratozoospermia in a consanguineous family. A homozygous CCDC103 variant segregating recessively with an infertility phenotype was identified (ENST00000035776.2, c.461A>C, p.His154Pro). CCDC103 p.His154Pro was previously reported as a high prevalence mutation causing PCD, though the reproductive phenotype of these PCD individuals is unknown. Transmission electron microscopy (TEM) of affected individuals' spermatozoa showed that the mid-piece was severely damaged with disorganized dynein arms, similar to the abnormal ultrastructure of respiratory ciliary of PCD individuals with the same mutation. Thus, our findings expand the phenotype spectrum of CCDC103 p.His154Pro as a novel pathogenic gene for nonsyndromic asthenospermia.
Asthenozoospermia/pathology* ; Dyneins/genetics* ; Homozygote ; Humans ; Male ; Microtubule-Associated Proteins ; Mutation ; Mutation, Missense ; Sperm Tail/metabolism*