DJ-1 regulates mitochondrial function and promotes retinal ganglion cell survival under high glucose-induced oxidative stress.

Purpose: This study aimed to investigate the antioxidative and neuroprotective effects of DJ-1 in mitigating retinal ganglion cell (RGC) damage induced by high glucose (HG). Methods: A diabetic mouse model and an HG-induced R28 cell model were employed for loss- and gain-of-function experiments. The expression levels of apoptosis and oxidative stress-related factors, including Bax, Bcl-2, caspase3, Catalase, MnSOD, GCLC, Cyto c, and GPx-1/2, were assessed in both animal and cell models using Western blotting. Retinal structure and function were evaluated through HE staining, electroretinogram, and RGC counting. Mitochondrial function and apoptosis were determined using JC-1 and TUNEL staining, and reactive oxygen species (ROS) measurement. Results: In the mouse model, hyperglycemia resulted in reduced retinal DJ-1 expression, retinal structural and functional damage, disrupted redox protein profiles, and mitochondrial dysfunction. Elevated glucose levels induced mitochondrial impairment, ROS generation, abnormal protein expression, and apoptosis in R28 cells. Augmenting DJ-1 expression demonstrated a restoration of mitochondrial homeostasis and alleviated diabetes-induced morphological and functional impairments both in vivo and in vitro. Conclusion: This study provides novel insights into the regulatory role of DJ-1 in mitochondrial dynamics, suggesting a potential avenue for enhancing RGC survival in diabetic retinopathy.

Leveraging cell death patterns to predict metastasis in prostate adenocarcinoma and targeting PTGDS for tumor suppression.

Metastasis is the major cause of treatment failure in patients with prostate adenocarcinoma (PRAD). Diverse programmed cell death (PCD) patterns play an important role in tumor metastasis and hold promise as predictive indicators for PRAD metastasis. Using the LASSO Cox regression method, we developed PCD score (PCDS) based on differentially expressed genes (DEGs) associated with PCD. Clinical correlation, external validation, functional enrichment analysis, mutation landscape analysis, tumor immune environment analysis, and immunotherapy analysis were conducted. The role of Prostaglandin D2 Synthase (PTGDS) in PRAD was examined through in vitro experiments, single-cell, and Mendelian randomization (MR) analysis. PCDS is elevated in patients with higher Gleason scores, higher T stage, biochemical recurrence (BCR), and higher prostate-specific antigen (PSA) levels. Individuals with higher PCDS are prone to metastasis, metastasis after BCR, BCR, and castration resistance. Moreover, PRAD patients with low PCDS responded positively to immunotherapy. Random forest analysis and Mendelian randomization analysis identified PTGDS as the top gene associated with PRAD metastasis and in vitro experiments revealed that PTGDS was considerably downregulated in PRAD cells against normal prostate cells. Furthermore, the overexpression of PTGDS was found to suppress the migration, invasion, proliferationof DU145 and LNCaP cells. To sum up, PCDS may be a useful biomarker for forecasting the possibility of metastasis, recurrence, castration resistance, and the efficacy of immunotherapy in PRAD patients. Additionally, PTGDS was identified as a viable therapeutic target for the management of PRAD.

Cellular RNA-binding proteins LARP4 and PABPC1 synergistically facilitate viral translation of coronavirus PEDV.

Coronaviruses are causing epizootic diseases and thus are a substantial threat for both domestic and wild animals. These viruses depend on the host translation machinery to complete their life cycle. The current paper identified cellular RNA-binding proteins (RBPs), La-related protein 4 (LARP4) and polyadenylate-binding protein cytoplasmic 1 (PABPC1), as critical regulators of efficient translation of the coronavirus porcine epidemic diarrhea virus (PEDV) mRNA. In Vero cells, PEDV infection caused LARP4 to migrate from the nucleus to the cytoplasm in a chromosome region maintenance1 (CRM1)-independent pathway. In the absence of the nuclear export signal of LARP4, viral translation was not promoted by LARP4. A further study unveiled that the cytoplasmic LARP4 binds to the 3'-terminal untranslated region (3'UTR) of PEDV mRNA with the assistance of PABPC1 to facilitate viral translation. LARP4 knockdown reduced the promotion of the PABPC1-induced 3'UTR translation activity. Moreover, the rabbit reticulocyte lysate (RRL) system revealed that the prokaryotic expressed protein LARP4 and PABPC1 enhance PEDV mRNA translation. To our knowledge, this is the first study demonstrating that PEDV induces nucleo-cytoplasmic shuttling of LARP4 to enhance its own replication, which broadens our insights into how viruses use host's RBPs for the efficient translation of viral mRNA.

Catalytic Conversion of NO and CO by Noble-Metal-Free Copper-Vanadium Oxide Cluster Anions CuVO3,4.

Herein, by using state-of-the-art mass spectrometry, we demonstrated experimentally that the bimetallic copper-vanadium oxide cluster anions CuVO3,4- can catalyze the reduction of NO by CO into N2O and CO2. Note that the catalysis of NO reduction by CO has been rarely established in the gas phase and noble-metal containing clusters were commonly emphasized. Benefiting from quantum-chemical calculations, the Cu-V synergistic effect that both metal atoms work energetically to favor NO adsorption, N-N coupling, and CO oxidation by facilitating electron transfer can be understood at a strictly molecular level. Theoretical results demonstrated that the precaptured NO molecule encourages the adsorption of the second NO by electron donation. This finding deepens our understanding on NO reduction that NO functions not only as a reactant but also as a promoter during the reactions. This discovery could be helpful to permeate the nature and mechanism of active sites on related copper-vanadium heterogeneous catalyst used in real-life NO reduction.

A New Technique of Sterile Polyethylene Instrument Cover as Temporary Corneal Patch to Reconstruct Anterior Chamber in Penetrating Keratoplasty Combined with Intraocular Surgery.

PURPOSE: Complications such as explosive choroidal hemorrhage, residual cortex, and capsule rupture often occur during intraocular surgery combined with penetrating keratoplasty (PKP) due to poor maintenance of the anterior chamber. To address these challenges, we have innovatively utilized a sterile polyethylene instrument cover to temporarily reconstruct the anterior chamber. METHODS: In this report, we describe a technique where a 'temporary corneal patch' was created from a sterile instrument cover, using a trephine to ensure a diameter approximately 1-2 mm wider than the corneal bed or perforation. This patch was then sutured into the host corneal bed or the perforation with 10-0 nylon sutures during intraocular surgery combined with PKP. Each case was evaluated for surgical efficacy and complications. RESULTS: We successfully applied this technique in three cases of combined corneal transplantation surgery. In two cases, PKP, phacoemulsification, and intraocular lens implantation were successfully performed. In the third case, PKP, vitrectomy, and other intraocular procedures were performed. No intraoperative or postoperative complications were observed. CONCLUSION: The use of a sterile polyethylene material as a temporary corneal patch for anterior chamber reconstruction represents a safe, effective, and cost-efficient approach for intraocular surgery combined with PKP or posterior segment surgery as a keratoprosthesis.

Multi-omics Mendelian randomization integrating GWAS, eQTL and pQTL data revealed GSTM4 as a potential drug target for migraine.

INTRODUCTION: Migraine, as a complex neurological disease, brings heavy burden to patients and society. Despite the availability of established therapies, existing medications have limited efficacy. Thus, we aimed to find the drug targets that improve the prognosis of migraine. METHOD: We used Mendelian Randomization (MR) and Summary-data-based MR (SMR) analyses to study possible drug targets of migraine by summary statistics from FinnGen cohorts (nCase = 44,616, nControl = 367,565), with further replication in UK Biobank (nCase = 26,052, nControl = 487,214). Genetic instruments were obtained from eQTLGen and UKB-PPP to verify the drug targets at the gene expression and protein levels. The additional analyses including Bayesian co-localization, the heterogeneity in dependent instruments(HEIDI), Linkage Disequilibrium Score(LDSC), bidirectional MR, multivariate MR(MVMR), heterogeneity test, horizontal pleiotropy test, and Steiger filtering were implemented to consolidate the findings further. Lastly, drug prediction analysis and phenome-wide association study(PheWAS) were employed to imply the possibility of drug targets for future clinical applications. RESULT: The MR analysis of eQTL data showed that four drug targets (PROCR, GSTM4, SLC4A1, and TNFRSF10A) were significantly associated with migraine risk in both the FinnGen and UK Biobank cohorts. However, only GSTM4 exhibited consistent effect directions across the two outcomes(Discovery cohort: OR(95%CI) = 0.94(0.93-0.96); p = 2.70e - 10; Replication cohort: OR(95%CI) = 0.93(0.91-0.94); p = 4.21e - 17). Furthermore, GSTM4 passed the SMR at p < 0.05 and HEIDI test at p > 0.05 at both the gene expression and protein levels. The protein-level MR analysis revealed a strong correlation between genetically predicted GSTM4 with a lower incidence of migraine and its subtypes(Overall migraine: OR(95%CI) = 0.91(0.87-0.95); p = 6.98e-05; Migraine with aura(MA): OR(95%CI) = 0.90(0.85-0.96); p = 2.54e-03; Migraine without aura(MO): OR(95%CI) = 0.90(0.83-0.96); p = 2.87e-03), indicating a strong co-localization relationship (PPH4 = 0.86). Further analyses provided additional validation for the possibility of GSTM4 as a migraine treatment target. CONCLUSION: This study identifies GSTM4 as a potential druggable gene and promising therapeutic target for migraine.

Continuous-wave quasi-single-mode random lasing in CH3NH3PbBr3 perovskite films on patterned sapphire substrates.

We report intriguing continuous-wave quasi-single-mode random lasing in methylammonium lead bromide (CH3NH3PbBr3) perovskite films synthesized on a patterned sapphire substrate (PSS) under excitation of a 532-nm laser diode. The random laser emission evolves from a typical multi-mode to a quasi-single-mode with increasing pump fluences. The full width at half-maximum of the lasing peak is as narrow as 0.06 nm at ∼547.8 nm, corresponding to a high Q-factor of ∼9000. Such excellent random lasing performance is plausibly ascribed to the exciton resonance in optical absorption at 532 nm and the enhanced optical resonance due to the increased likelihood for randomly scattered light to re-enter the optical loops formed among the perovskite grains by multi-reflection at the perovskite/PSS interfaces. This work demonstrates the promise of single-mode perovskite random lasers by introducing the exciton resonance effect and ingeniously designed periodic nano/micro optical structure.

Epigenetic regulation in liver regeneration.

The liver is considered unique in its enormous capacity for regeneration and self-repair. In contrast to other regenerative organs (i.e., skin, skeletal muscle, and intestine), whether the adult liver contains a defined department of stem cells is still controversial. In order to compensate for the massive loss of hepatocytes following liver injury, the liver processes a precisely controlled transcriptional reprogram that can trigger cell proliferation and cell-fate switch. Epigenetic events are thought to regulate the organization of chromatin architecture and gene transcription during the liver regenerative process. In this review, we will summarize how changes to the chromatin by epigenetic modifiers are translated into cell fate transitions to restore liver homeostasis during liver regeneration.

Lattice Strain Engineering on Metal-Organic Frameworks by Ligand Doping to Boost the Electrocatalytic Biomass Valorization.

As an efficient and environmental-friendly strategy, electrocatalytic oxidation can realize biomass lignin valorization by cleaving its aryl ether bonds to produce value-added chemicals. However, the complex and polymerized structure of lignin presents challenges in terms of reactant adsorption on the catalyst surface, which hinders further refinement. Herein, NiCo-based metal-organic frameworks (MOFs) are employed as the electrocatalyst to enhance the adsorption of reactant molecules through π-π interaction. More importantly, lattice strain is introduced into the MOFs via curved ligand doping, which enables tuning of the d-band center of metal active sites to align with the reaction intermediates, leading to stronger adsorption and higher electrocatalytic activity toward bond cleavage within lignin model compounds and native lignin. When 2'-phenoxyacetophenone is utilized as the model compound, high yields of phenol (76.3%) and acetophenone (21.7%) are achieved, and the conversion rate of the reactants reaches 97%. Following pre-oxidation of extracted poplar lignin, >10 kinds of phenolic compounds are received using the as-designed MOFs electrocatalyst, providing ≈12.48% of the monomer, including guaiacol, vanillin, eugenol, etc., and p-hydroxybenzoic acid dominates all the products. This work presents a promising and deliberately designed electrocatalyst for realizing lignin valorization, making significant strides for the sustainability of this biomass resource.

A new perspective: deciphering the aberrance and clinical implication of disulfidptosis signatures in clear cell renal cell carcinoma.

Recent research has discovered disulfidptosis as a form of programmed cell death characterized by disulfide stress. However, its significance in clear cell renal cell carcinoma (ccRCC) remains unclear. To investigate this, data from The Cancer Genome Atlas were collected and used to identify ccRCC subgroups. Unsupervised clustering was employed to determine ccRCC heterogeneity. The mutation landscape and immune microenvironment of the subgroups were analyzed. The Disulfidptosis-Related Score was calculated using the LASSO-penalized Cox regression algorithm. The E-MATB-1980 cohort was used to validate the signature. The role of SLC7A11 in ccRCC metastasis was explored using western blotting and Transwell assays. Disulfidptosis-related genes are commonly downregulated in cancers and are linked to hypermethylation and copy number variation. The study revealed that ccRCC is divided into two sub-clusters: the disulfidptosis-desert sub-cluster, which is associated with a poor prognosis, a higher mutation frequency, and an immunosuppressive microenvironment. A 14-gene prognostic model was developed using differentially expressed genes and was validated in the E-MATB-1980 cohort. The low-risk group demonstrated longer overall and disease-free survival and responded better to targeted immunotherapy. Results from in vitro experiments identified SLC7A11 as a key participant in ccRCC metastasis.

Joint effect of abnormal systemic immune-inflammation index (SII) levels and diabetes on cognitive function and survival rate: A population-based study from the NHANES 2011-2014.

OBJECTIVE: The purpose of this study was to investigate whether the combination of abnormal systemic immune-inflammation index (SII) levels and hyperglycemia increased the risk of cognitive function decline and reduced survival rate in the United States. METHODS: This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) database from 2011-2014 and enrolled 1,447 participants aged 60 years or older. Restricted cubic splines (RCS), linear regression and kaplan-meier(KM) curve were employed to explore the combined effects of abnormal SII and hyperglycemia on cognitive function and survival rate, and subgroup analysis was also conducted. RESULTS: The RCS analysis revealed an inverted U-shaped relationship between lgSII levels and cognitive function. Linear regression analysis indicated that neither abnormal SII nor diabetes alone significantly contributed to the decline in cognitive function compared to participants with normal SII levels and blood glucose. However, when abnormal SII coexisted with diabetes (but not prediabetes), it resulted to a significant decline in cognitive function. After adjusting for various confounding factors, these results remained significant in Delayed Word Recall (ß:-0.76, P<0.05) and Digit Symbol Substitution tests (ß:-5.02, P<0.05). Nevertheless, these results showed marginal significance in Total Word Recall test as well as Animal Fluency test. Among all subgroup analyses performed, participants with both abnormal SII levels and diabetes exhibited the greatest decline in cognitive function compared to those with only diabetes. Furthermore, KM curve demonstrated that the combination of abnormal SII levels and diabetes decreased survival rate among participants. CONCLUSION: The findings suggest that the impact of diabetes on cognitive function/survival rate is correlated with SII levels, indicating that their combination enhances predictive power.

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes.

BACKGROUND: Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. METHODS: Transcriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the "Enrichr" platform was utilized to identify potential drugs for MDD. RESULTS: Totally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. CONCLUSION: Our work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

Associations between preparedness, perceived stress, depression, and quality of life in family caregivers of patients with a temporary enterostomy.

PURPOSE: To investigate the preparedness, perceived stress, risk of depression, and quality of life of family caregivers of patients receiving a temporary enterostomy, to provide a reference for improving the long-term care and quality of life of patients receiving a temporary enterostomy. METHODS: We enrolled 181 family caregivers of patients in a hospital in China from 2021 to 2023. Responses to the General Information Questionnaire, the Chinese Caregiver Preparedness Scale, the Chinese Perceived Stress Scale, the Chinese bilingual version of the Patient Health Questionnaire-2, and the 12-item Short Form Survey were collected online. RESULTS: Pearson's correlation analysis revealed that family caregivers' risk of depression was negatively correlated with their preparedness, the physical component summary score, and the mental component summary score but was positively correlated with perceived stress. Multiple linear regression analysis identified factors influencing caregiver preparedness. CONCLUSIONS: These findings help healthcare personnel to identify high-risk individuals among family caregivers of patients receiving a temporary enterostomy. This provides a basis for formulating well-planned, dynamic health education programs that meet patients' needs for disease-related knowledge and care.

Automatic Segmentation of Bone Marrow Lesions on MRI Using a Deep Learning Method.

Bone marrow lesion (BML) volume is a potential biomarker of knee osteoarthritis (KOA) as it is associated with cartilage degeneration and pain. However, segmenting and quantifying the BML volume is challenging due to the small size, low contrast, and various positions where the BML may occur. It is also time-consuming to delineate BMLs manually. In this paper, we proposed a fully automatic segmentation method for BMLs without requiring human intervention. The model takes intermediate weighted fat-suppressed (IWFS) magnetic resonance (MR) images as input, and the output BML masks are evaluated using both regular 2D Dice similarity coefficient (DSC) of the slice-level area metric and 3D DSC of the subject-level volume metric. On a dataset with 300 subjects, each subject has a sequence of 36 IWFS MR images approximately. We randomly separated the dataset into training, validation, and testing sets with a 70%/15%/15% split at the subject level. Since not every subject or image has a BML, we excluded the images without a BML in each subset. The ground truth of the BML was labeled by trained medical staff using a semi-automatic tool. Compared with the ground truth, the proposed segmentation method achieved a Pearson's correlation coefficient of 0.98 between the manually measured volumes and automatically segmented volumes, a 2D DSC of 0.68, and a 3D DSC of 0.60 on the testing set. Although the DSC result is not high, the high correlation of 0.98 indicates that the automatically measured BML volume is strongly correlated with the manually measured BML volume, which shows the potential to use the proposed method as an automatic measurement tool for the BML biomarker to facilitate the assessment of knee OA progression.

Biomimetic Dual-Network Collagen Fibers with Porous and Mechanical Cues Reconstruct Neural Stem Cell Niche via AKT/YAP Mechanotransduction after Spinal Cord Injury.

Tissue engineering scaffolds can mediate the maneuverability of neural stem cell (NSC) niche to influence NSC behavior, such as cell self-renewal, proliferation, and differentiation direction, showing the promising application in spinal cord injury (SCI) repair. Here, dual-network porous collagen fibers (PCFS) are developed as neurogenesis scaffolds by employing biomimetic plasma ammonia oxidase catalysis and conventional amidation cross-linking. Following optimizing the mechanical parameters of PCFS, the well-matched Young's modulus and physiological dynamic adaptability of PCFS (4.0 wt%) have been identified as a neurogenetic exciter after SCI. Remarkably, porous topographies and curving wall-like protrusions are generated on the surface of PCFS by simple and non-toxic CO2 bubble-water replacement. As expected, PCFS with porous and matched mechanical properties can considerably activate the cadherin receptor of NSCs and induce a series of serine-threonine kinase/yes-associated protein mechanotransduction signal pathways, encouraging cellular orientation, neuron differentiation, and adhesion. In SCI rats, implanted PCFS with matched mechanical properties further integrated into the injured spinal cords, inhibited the inflammatory progression and decreased glial and fibrous scar formation. Wall-like protrusions of PCFS drive multiple neuron subtypes formation and even functional neural circuits, suggesting a viable therapeutic strategy for nerve regeneration and functional recovery after SCI.

An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a common and lethal urological malignancy for which there are no effective personalized therapeutic strategies. Programmed cell death (PCD) patterns have emerged as critical determinants of clinical prognosis and immunotherapy responses. However, the actual clinical relevance of PCD processes in ccRCC is still poorly understood. METHODS: We screened for PCD-related gene pairs through single-sample gene set enrichment analysis (ssGSEA), consensus cluster analysis, and univariate Cox regression analysis. A novel machine learning framework incorporating 12 algorithms and 113 unique combinations were used to develop the cell death-related gene pair score (CDRGPS). Additionally, a radiomic score (Rad_Score) derived from computed tomography (CT) image features was used to classify the CDRGPS status as high or low. Finally, we conclusively verified the function of PRSS23 in ccRCC. RESULTS: The CDRGPS was developed through an integrated machine learning approach that leveraged 113 algorithm combinations. CDRGPS represents an independent prognostic biomarker for overall survival and demonstrated consistent performance between training and external validation cohorts. Moreover, CDRGPS showed better prognostic accuracy compared to seven previously published cell death-related signatures. In addition, patients classified as high-risk by CDRGPS exhibited increased responsiveness to tyrosine kinase inhibitors (TKIs), mammalian Target of Rapamycin (mTOR) inhibitors, and immunotherapy. The Rad_Score demonstrated excellent discrimination for predicting high versus low CDRGPS status, with an area under the curve (AUC) value of 0.813 in the Cancer Imaging Archive (TCIA) database. PRSS23 was identified as a significant factor in the metastasis and immune response of ccRCC, thereby validating experimental in vitro results. CONCLUSIONS: CDRGPS is a robust and non-invasive tool that has the potential to improve clinical outcomes and enable personalized medicine in ccRCC patients.

Unveiling potential drug targets for hyperparathyroidism through genetic insights via Mendelian randomization and colocalization analyses.

Hyperparathyroidism (HPT) manifests as a complex condition with a substantial disease burden. While advances have been made in surgical interventions and non-surgical pharmacotherapy for the management of hyperparathyroidism, radical options to halt underlying disease progression remain lacking. Identifying putative genetic drivers and exploring novel drug targets that can impede HPT progression remain critical unmet needs. A Mendelian randomization (MR) analysis was performed to uncover putative therapeutic targets implicated in hyperparathyroidism pathology. Cis-expression quantitative trait loci (cis-eQTL) data serving as genetic instrumental variables were obtained from the eQTLGen Consortium and Genotype-Tissue Expression (GTEx) portal. Hyperparathyroidism summary statistics for single nucleotide polymorphism (SNP) associations were sourced from the FinnGen study (5590 cases; 361,988 controls). Colocalization analysis was performed to determine the probability of shared causal variants underlying SNP-hyperparathyroidism and SNP-eQTL links. Five drug targets (CMKLR1, FSTL1, IGSF11, PIK3C3 and SLC40A1) showed significant causation with hyperparathyroidism in both eQTLGen and GTEx cohorts by MR analysis. Specifically, phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3) and solute carrier family 40 member 1 (SLC40A1) showed strong evidence of colocalization with HPT. Multivariable MR and Phenome-Wide Association Study analyses indicated these two targets were not associated with other traits. Additionally, drug prediction analysis implies the potential of these two targets for future clinical applications. This study identifies PIK3C3 and SLC40A1 as potential genetically proxied druggable genes and promising therapeutic targets for hyperparathyroidism. Targeting PIK3C3 and SLC40A1 may offer effective novel pharmacotherapies for impeding hyperparathyroidism progression and reducing disease risk. These findings provide preliminary genetic insight into underlying drivers amenable to therapeutic manipulation, though further investigation is imperative to validate translational potential from preclinical models through clinical applications.

Can the allocation of primary health care system resources affect efficiency? A spatial Dubin model study in China.

BACKGROUND: The primary health care (PHC) system plays an important role in China's health care system, but there are challenges such as irrational allocation of health resources and inefficient operation, which need to be improved. The purpose of this study was to explore the impact of resource allocation on the efficiency of the PHC system in China. METHODS: The data in 31 provinces were collected from the China Statistical Yearbook 2017-2021 and the China Health Statistical Yearbook 2017-2021. The comprehensive health resource density index (CHRDI) was constructed based on the entropy method and the health resource density index (HRDI), which was used to analyze the allocation of primary health resources in each province. The adjusted efficiency of the PHC system in each province was calculated by the bootstrap data envelopment analysis (DEA). Finally, the spatial Dubin model was used to explore the effect of the CHRDI on efficiency. RESULTS: From 2016 to 2020, the allocation of primary health resources in 31 provinces showed an increasing trend, and the average efficiency after correction showed a decreasing state year by year. The spatial direct effect and spatial spillover effect coefficients of CHRDI were 0.820 and 1.471, which positively affect the efficiency. Per capita Gross Domestic Product (GDP), urbanization rate, and the proportion of the elderly were the factors affecting the efficiency of the PHC system. CONCLUSIONS: The allocation of primary health resources in all provinces in China has improved each year, but there are still great differences, and efficiency must be further improved. Pay attention to the spatial spillover effect of the level of resource allocation and formulate differentiated measures for different regions. Attention should also be paid to the impact of population aging and economic development on the utilization of primary health resources by increasing health needs and choices.

Exploring CCL11 in breast cancer: unraveling its anticancer potential and immune modulatory effects involving the Akt-S6 signaling.

BACKGROUND: CCL11, a chemokine known for recruiting immune cells to the tumor microenvironment (TME), has an unclear role in the context of its expression, patient prognosis, and the presence of tumor-infiltrating immune cells (TILs) in breast cancer. METHODS: The expression of CCL11 in invasive breast cancer (BRCA) was analyzed using TCGA database. Survival curve and Cox regression analysis determined the potential of CCL11 as an independent prognostic indicator. GSEA performed functional analysis on genes related to CCL11. CIBERSORT algorithm quantified the infiltration level of immune cells with varying CCL11 expression. Lastly, the correlation between CCL11 expression and anticancer drug sensitivity was examined. Immunohistochemistry (IHC) and qRT-PCR confirmed CCL11 expression in clinical tissue samples. The anti-tumor efficacy of CCL11 was investigated using CCK-8, plate formation, transwell assay, and Western blot. RESULTS: CCL11 expression was elevated in BRCA tumor tissues compared to adjacent normal tissues. Recurrence-free survival (RFS) was longer in patients with high expression of CCL11. Enrichment and co-expression analyses revealed CCL11's association with numerous immune-related signaling pathways and genes. Validation studies confirmed high CCL11 expression in breast cancer tissues. In vitro experiments substantiated CCL11's anticancer effects in BRCA. CONCLUSION: CCL11 expression correlates with immune cell infiltration in breast cancer, indicating its potential as a prognostic biomarker for BRCA.

Virtual group photography based on scene and person perception.

Group photos have become indispensable in various gathering scenarios, such as family reunions, friends' gatherings, competitions, conferences, store openings, and school graduation ceremonies. The researchers tried automatically adding people who could not participate in the group photo. However, the current research on generating the pose or position of the person by context prediction of the group photo ignores the individual attributes (such as height and body shape) of the target person and does not consider the pose and boundary of the person at the same time. To address these issues, we propose a virtual group photography model that combines the global context of a group photo and the individual attributes of the target person. The model is divided into two stages. The first stage is to predict the person's position, pose, and boundary in the new group photo based on the context of the input group photo and the person's characteristics. The second stage generates new group photos based on the first stage's pose and boundary results. The experimental results show that our method can significantly improve the harmony and authenticity of the synthesis of people in group photos and synthesize the characters that should exist in the group photo, which is very suitable for the field of group photos.