Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.

Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans ; Male ; Azoospermia/genetics* ; Meiosis/genetics* ; Spermatogenesis/genetics* ; Adult ; Exome Sequencing ; Microtubule-Associated Proteins/genetics* ; Alleles ; Infertility, Male/genetics*

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis* ; Animals ; Drug Resistant Epilepsy/drug therapy* ; Electroencephalography/methods* ; Rats ; Anticonvulsants/pharmacology* ; Neural Networks, Computer ; Male ; Humans ; Phenytoin/pharmacology* ; Adult ; Disease Models, Animal ; Female ; Rats, Sprague-Dawley ; Young Adult ; Convolutional Neural Networks

OBJECTIVE: Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection frequently develop central nervous system damage, yet the mechanisms driving this pathology remain unclear. This study investigated the primary pathways and key factors underlying brain tissue damage induced by the SARS-CoV-2 beta variant (lineage B.1.351). METHODS: K18-hACE2 and C57BL/6 mice were intranasally infected with the SARS-CoV-2 beta variant. Viral replication, pathological phenotypes, and brain transcriptomes were analyzed. Gene Ontology (GO) analysis was performed to identify altered pathways. Expression changes of host genes were verified using reverse transcription-quantitative polymerase chain reaction and Western blot. RESULTS: Pathological alterations were observed in the lungs of both mouse strains. However, only K18-hACE2 mice exhibited elevated viral RNA loads and infectious titers in the brain at 3 days post-infection, accompanied by neuropathological injury and weight loss. GO analysis of infected K18-hACE2 brain tissue revealed significant dysregulation of genes associated with innate immunity and antiviral defense responses, including type I interferons, pro-inflammatory cytokines, Toll-like receptor signaling components, and interferon-stimulated genes. Neuroinflammation was evident, alongside activation of apoptotic and pyroptotic pathways. Furthermore, altered neural cell marker expression suggested viral-induced neuroglial activation, resulting in caspase 4 and lipocalin 2 release and disruption of neuronal molecular networks. CONCLUSION These findings elucidate mechanisms of neuropathogenicity associated with the SARS-CoV-2 beta variant and highlight therapeutic targets to mitigate COVID-19-related neurological dysfunction.
Animals ; COVID-19/genetics* ; Mice ; Brain/metabolism* ; Apoptosis ; Mice, Inbred C57BL ; SARS-CoV-2/physiology* ; Pyroptosis ; Gene Expression Profiling ; Transcriptome ; Male ; Female

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.