Clinical characteristics and status of treatment of small-cell carcinoma of the ovary, hypercalcemic type in the Chinese population: a meta-analysis.

OBJECTIVE: This study aimed to comprehensively analyze the clinical characteristics and treatment status of Chinese small cell carcinoma of the ovary hypercalcemic type (SCCOHT) patients, providing insights into this unique population and comparing findings with international literature. METHODS: Through a meta-analysis, we collected data from published case reports and records from the Obstetrics & Gynecology Hospital of Fudan University. Demographic information, clinical presentations, tumor attributes, treatment modalities, and survival outcomes were extracted and examined alongside relevant global studies. RESULTS: The analysis encompassed 80 Chinese SCCOHT patients, of which 62 from 33 previously reported literatures, and the other 18 were from Obstetrics & Gynecology Hospital of Fudan University. In 62 cases with stage information, A total of 25 tumors were International Federation of Gynecology and Obstetrics stage I, 3 were stage II, 19 were stage III, and 15 were stage IV. Most patients received surgery and chemotherapy, but regimens were varied. Median follow-up was 10 months (range=4-120). Elevated carbohydrate antigen 125 and serum calcium levels were consistent findings. Recurrence rates were notable, especially among stage I patients. Platinum-based chemotherapy, paclitaxel and carboplatin (n=11, 13.4%), constituted common treatment regimens. CONCLUSION: This study observed demographic and clinical similarities with international datasets. And the findings emphasize the urgency for innovative therapeutic approaches to improve outcomes in SCCOHT patients. Continued research efforts are essential to enhance the knowledge surrounding this rare malignancy and to optimize its clinical management.

Testis specific protein, Y-encoded-like 2 activates JAK2/STAT3 pathway in hypothalamic paraventricular nucleus to sustain hypertension.

BACKGROUND: In the hypothalamic paraventricular nucleus (PVN) of spontaneously hypertensive rats (SHRs), the expression of Testis specific protein, Y-encoded-like 2 (TSPYL2) and the phosphorylation level of Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) are higher comparing with the normotensive Wistar-Kyoto rats (WKY). But how they are involved in hypertension remains unclear. TSPYL2 may interact with JAK2/STAT3 in PVN to sustain the high blood pressure during hypertension. METHODS: Knockdown of TSPYL2 via adeno-associated virus (AAV) carrying shRNA was conducted through bilateral micro-injection into the PVN of SHR and WKY rats. JAK2/STAT3 inhibition was achieved by intraperitoneally or PVN injection of AG490 into the SHRs. Blood pressure (BP), plasma norepinephrine (NE), PVN inflammatory response, and PVN oxidative stress were measured. RESULTS: TSPYL2 knock-down in the PVN of SHRs but not WKYs led to reduced BP and plasma NE, and deactivation of JAK2/STAT3, decreased expression of pro-inflammatory cytokine IL-1ß, and increased expression of anti-inflammatory cytokine IL-10 in the PVN. Meanwhile, AG490 administrated in both ways reduced the blood pressure in the SHRs and deactivated JAK2/STAT3 but failed to change the expression of TSPYL2 in PVN. AG490 also downregulated expression of IL-1ß and upregulated expression of IL-10. Both knockdown of TSPYL2 and inhibition of JAK2/STAT3 can reduce the oxidative stress in the PVN of SHRs. CONCLUSION: JAK2/STAT3 is regulated by TSPYL2 in the PVN of SHRs, and PVN TSPYL2/JAK2/STAT3 is essential for maintaining high blood pressure in the hypertensive rats, making it a potential therapeutic target for hypertension.

Alpha-tocopherol inhibits ferroptosis and promotes neural function recovery in rats with spinal cord injury via downregulating Alox15.

Spinal cord injury (SCI) is a type of central nervous system (CNS) injury in which ferroptosis is becoming a promising target for treatment. Alpha-tocopherol (Vitamin E, Vit E) is a compound with anti-ferroptosis activity. The mechanism of alpha-tocopherol in regulating ferroptosis after SCI has not been deeply studied. In this study, rats with SCI were treated by Alpha-tocopherol based on bioinformatic analysis and molecular docking prediction. Behavioral tests and histological findings showed that Alpha-tocopherol promoted neural function recovery and tissue repairment in rats with SCI. Subsequently, regulatory effects of Alpha-tocopherol on Alox15 and ferroptosis were detected and then localized by immunofluorescence. In vitro, alpha-tocopherol improved the ROS accumulation, iron overload, lipid peroxidation and mitochondrial dysfunction. The effects of Alpha-tocopherol on the expression of Alox15, Ptgs2 and 4Hne were validated in vitro. Finally, the inhibitory effects of Alpha-tocopherol on Alox15 and ferroptosis were weakened by the mutation of 87th residue of Alox15. In summary, alpha-tocopherol could alleviate SCI-induced ferroptosis by downregulating Alox15 to promote neural function recovery in rats with SCI. Findings in this study could help further our understanding on SCI-induced ferroptosis and provide a novel insight for treating SCI.

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.

BACKGROUND: Clinical notes contain contextualized information beyond structured data related to patients' past and current health status. OBJECTIVE: This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data. METHODS: Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors. RESULTS: The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments. CONCLUSIONS: The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

Structural characterization and prebiotic activity of rhamnogalacturonan-I rich pumpkin pectic polysaccharide extracted by alkaline solution.

The present study aimed to investigate the structural and physicochemical characteristics of alkali-extracted pectic polysaccharide (AkPP) and to evaluate its prebiotic effects. AkPP was obtained from pumpkin pulp using an alkaline extraction method. AkPP, which had a molecular weight (Mw) of mainly 13.67 kDa and an esterification degree of 9.60%, was composed mainly of galacturonic acid (GalA), rhamnose (Rha), galactose, and arabinose. The ratio of the homogalacturonan (HG) region to the rhamnogalacturonan-I (RG-I) region in AkPP was 48.74:43.62. In the nuclear magnetic resonance spectrum, the signals indicating α-1,4-linked D-GalA, α-1,2-linked L-Rha, α-1,2,4-linked L-Rha residues were well resolved, demonstrating the presence of the HG and RG-I regions in its molecular structure. Collectively, AkPP was low methoxyl pectin rich in the RG-I region with short side chains and had a low Mw. Thermal analysis revealed that AkPP had good thermal stability. Compared to inulin, AkPP more effectively promoted the proliferation of Lactobacillus acidophilus, Lacticaseibacillus rhamnosus GG, Lacticaseibacillus casei, and Lacticaseibacillus paracasei and the production of lactic, acetic, and propionic acids. This study presents the unique structural features of AkPP and provides a scientific basis for further investigation of the potential of AkPP as a promising prebiotic.

The Expression of TP63 as a Biomarker of Early Recurrence in Resected Esophageal Squamous Cell Carcinoma after Neoadjuvant Chemoradiotherapy.

Esophageal cancer ranks among the ten most common cancers worldwide. Despite the adoption of neoadjuvant concurrent chemoradiotherapy (nCCRT) followed by surgery as the standard treatment approach in recent years, the local recurrence rate remains high. In this study, we employed RNA-seq to investigate distinctive gene expression profiles in esophageal squamous cell carcinoma (ESCC) with or without recurrence following a standard treatment course. Our findings indicate that recurrent ESCC exhibits heightened keratinizing and epidermis development activity compared to non-recurrent ESCC. We identified TP63 as a potential candidate for distinguishing clinical outcomes. Furthermore, immunohistochemistry confirmed the trend of TP63 overexpression in ESCC recurrence. Patients with elevated TP63 expression had poorer overall survival and lower 3-year recurrence-free survival. This study underscores the potential of TP63 as a biomarker for detecting cancer recurrence and suggests its role in guiding future treatment options.

Genome-wide characterization of LEA gene family reveals a positive role of BnaA.LEA6.a in freezing tolerance in rapeseed (Brassica napus L.).

BACKGROUND: Freezing stress is one of the major abiotic stresses that causes extensive damage to plants. LEA (Late embryogenesis abundant) proteins play a crucial role in plant growth, development, and abiotic stress. However, there is limited research on the function of LEA genes in low-temperature stress in Brassica napus (rapeseed). RESULTS: Total 306 potential LEA genes were identified in B. rapa (79), B. oleracea (79) and B. napus (148) and divided into eight subgroups. LEA genes of the same subgroup had similar gene structures and predicted subcellular locations. Cis-regulatory elements analysis showed that the promoters of BnaLEA genes rich in cis-regulatory elements related to various abiotic stresses. Additionally, RNA-seq and real-time PCR results indicated that the majority of BnaLEA family members were highly expressed in senescent tissues of rapeseed, especially during late stages of seed maturation, and most BnaLEA genes can be induced by salt and osmotic stress. Interestingly, the BnaA.LEA6.a and BnaC.LEA6.a genes were highly expressed across different vegetative and reproductive organs during different development stages, and showed strong responses to salt, osmotic, and cold stress, particularly freezing stress. Further analysis showed that overexpression of BnaA.LEA6.a increased the freezing tolerance in rapeseed, as evidenced by lower relative electrical leakage and higher survival rates compared to the wild-type (WT) under freezing treatment. CONCLUSION: This study is of great significance for understanding the functions of BnaLEA genes in freezing tolerance in rapeseed and offers an ideal candidate gene (BnaA.LEA6.a) for molecular breeding of freezing-tolerant rapeseed cultivars.

Amine-Terminated Phenanthroline Diimides as Aqueous Masking Agents for Am(III)/Eu(III) Separation: An Alternative Ligand Design Strategy for Water-Soluble Lanthanide/Actinide Chelating Ligands.

Selective actinide coordination (from lanthanides) is critical for both nuclear waste management and sustainable development of nuclear power. Hydrophilic ligands used as masking agents to withhold actinides in the aqueous phase are currently highly pursued, while synthetic accessibility, water solubility, acid resistance, and extraction capability are the remaining problems. Most reported hydrophilic ligands are only effective at low acidity. We recently proved that the phenanthroline diimide skeleton was an efficient building block for the construction of highly efficient acid-resistant hydrophilic lanthanide/actinide separation agents, while the limited water solubility hindered the loading capability of the ligand. Herein, amine was introduced as the terminal solubilizing group onto the phenanthroline diimide backbone, which after protonation in acid showed high water solubility. The positively charged terminal amines enhanced the ligand water solubility to a large extent, which, on the other side, was believed to be detrimental for the coordination and complexation of the metal cations. We showed that by delicately adjusting the alkyl chain spacing, this intuitive disadvantage could be relieved and superior extraction performances could be achieved. This work holds significance for both hydrophilic lanthanide/actinide separation ligand design and, concurrently, offers insights into the development of water-soluble lanthanide/actinide complexes for biomedical and bioimaging applications.

Reverse remodeling of mitral leaflets after medical treatment in recent-onset dilated cardiomyopathy.

BACKGROUND: The growth of mitral leaflets (MLs) adaptive to left ventricluar (LV) remodeling has been observed. However, the elasticity of MLs upon mechanical stimuli would be supposed if it shrinks with LV reverse remodeling (LVRR). MATERIAL AND METHODS: Patients with idiopathic recent-onset dilated cardiomyopathy (RODCM) (n = 82) and 50 matched normal controls (NC) were prospectively enrolled. Echocardiography was performed at baseline and 6 months of follow-up for the anterior and posterior mitral leaflet (AML and PML) length, mitral annular dimension (MAD), and tenting height (TH). LVRR was measured as a ≥ 15% reduction in LV end-diastolic volume (LVEDV). RESULTS: After 6 months, LVRR was achieved in 69.5% of patients. The AML (28 ± 3 vs. 26 ± 3 mm, p = 0.004) and PML (19 ± 4 vs. 17 ± 3 mm, p < 0.001) decreased in length, as well as the MAD (31 ± 5 vs. 28 ± 5 mm, p = 0.001) and TH (10 ± 3 vs. 8 ± 2 mm, p < 0.001). Compared with the NC group, the AML and PML of the RODCM group were 16.7% and 35.7% longer at baseline and remained 8.3% and 21.2% longer at follow-up, respectively. The change in AML or PML correlated moderately with that in LVEDV (r = 0.487, p < 0.001; r = 0.516, p < 0.001, respectively). The AML and PML length decreased in the LVRR (+) subgroup (AML, 28 ± 3 vs. 26 ± 3 mm, p = 0.001; PML, 20 ± 4 vs. 16 ± 3 mm, p < 0.001), but remained the same in the LVRR (-) subgroup (27 ± 4 vs. 28 ± 4 mm, p = 0.318; 17 ± 3 vs. 17 ± 3 mm, p = 0.790). CONCLUSIONS: Enlarged MLs could reverse accompanied by LV reverse remodeling. This study provided the other facet of ML plasticity adaptive to mechanical stretching.

Sodium Glucose Co-transporter 2 Inhibitors and Major Adverse Cardiovascular Outcomes: A SMART-C Collaborative Meta-Analysis.

BACKGROUND: Sodium glucose co-transporter 2 inhibitors (SGLT2i) consistently improve heart failure and kidney-related outcomes; however, effects on major adverse cardiovascular events (MACE) across different patient populations are less clear. METHODS: This was a collaborative trial-level meta-analysis from the SGLT2i meta-analysis cardio-renal trialists consortium, which includes all phase 3, placebo-controlled, outcomes trials of SGLT2i across three patient populations (diabetes at high risk for atherosclerotic cardiovascular disease [ASCVD], heart failure [HF], or chronic kidney disease [CKD]). The outcomes of interest were MACE (composite of CV death, myocardial infarction [MI], or stroke), individual components of MACE (inclusive of fatal and non-fatal events), all-cause mortality, and death subtypes. Effect estimates for SGLT2i vs. placebo were meta-analyzed across trials and examined across key subgroups (established ASCVD, prior MI, diabetes, prior HF, albuminuria, CKD stages and risk groups). RESULTS: A total of 78,607 patients across 11 trials were included: 42,568 (54.2%), 20,725 (26.4%), and 15,314 (19.5%) were included from trials of patients with diabetes at high risk for ASCVD, HF, or CKD, respectively. SGLT2i reduced the rate of MACE by 9% (HR 0.91 [95% CI 0.87-0.96], p<0.0001) with a consistent effect across all three patient populations (I2=0%) and across all key subgroups. This effect was primarily driven by a reduction in CV death (HR 0.86 [0.81-0.92], p<0.0001), with no significant effect for MI in the overall population (HR 0.95 [0.87-1.04], p=0.29), and no effect on stroke (HR 0.99 [0.91-1.07], p=0.77). The benefit for CV death was driven primarily by reductions in HF death and sudden cardiac death (HR 0.68 [0.46-1.02] and HR 0.86 [0.78-0.95], respectively) and was generally consistent across subgroups, with the possible exception of being more apparent in those with albuminuria (Pint=0.02). CONCLUSIONS: SGLT2i reduce the risk of MACE across a broad range of patients irrespective of ASCVD, diabetes, kidney function or other major clinical characteristics at baseline. This effect is driven primarily by a reduction of CV death, particularly HF and sudden cardiac death, without a significant effect on MI in the overall population, and no effect on stroke. These data may help inform selection for SGLT2i therapies across the spectrum of cardiovascular-kidney-metabolic disease.

Recent advances in the development of DprE1 inhibitors using AI/CADD approaches.

Tuberculosis (TB) is a global lethal disease caused by Mycobacterium tuberculosis (Mtb). The flavoenzyme decaprenylphosphoryl-ß-d-ribose 2'-oxidase (DprE1) plays a crucial part in the biosynthesis of lipoarabinomannan and arabinogalactan for the cell wall of Mtb and represents a promising target for anti-TB drug development. Therefore, there is an urgent need to discover DprE1 inhibitors with novel scaffolds, improved bioactivity and high drug-likeness. Recent studies have shown that artificial intelligence/computer-aided drug design (AI/CADD) techniques are powerful tools in the discovery of novel DprE1 inhibitors. This review provides an overview of the discovery of DprE1 inhibitors and their underlying mechanism of action and highlights recent advances in the discovery and optimization of DprE1 inhibitors using AI/CADD approaches.

Multiplexed Opto-Microfluidic Biosensing: Advanced Platform for Prostate Cancer Detection.

Cancer stands as a prominent global cause of mortality, necessitating early detection to augment survival rates and alleviate economic burdens on healthcare systems. In particular, prostate cancer (PCa), impacting 1.41 million men globally in 2020, accentuates the demand for sensitive and cost-effective detection methods beyond traditional prostate-specific antigen (PSA) testing. While clinical techniques exhibit limitations, biosensors emerge as compact, user-friendly alternatives to traditional laboratory approaches. However, existing biosensors predominantly concentrate on PSA detection, prompting the necessity for advancing toward multiplex sensing platforms. This study introduces a compact opto-microfluidic sensor featuring a substrate of gold nanospikes, fabricated via electrodeposition, for enhanced sensitivity. Embedded within a microfluidic chip, this nanomaterial enables the precise and concurrent measurement of PSA, alongside two complementary PCa biomarkers, matrix metalloproteinase-2 (MMP-2) and anti-α-methylacyl-CoA racemase (anti-AMACR) in diluted human plasma, offering a comprehensive approach to PSA analysis. Taking advantage of the localized surface plasmon resonance principle, this biosensor offers robustness and sensitivity in real sample analysis without the need for labeling agents. With the limit of detection at 0.22, 0.37, and 0.18 ng/mL for PSA, MMP-2, and anti-AMACR, respectively, this biosensing platform holds promise for point-of-care analysis, underscoring its potential impact on medical diagnostics.

Healing with precision: A multi-functional hydrogel-bioactive glass dressing boosts infected wound recovery and enhances neurogenesis in the wound bed.

Chronic skin wounds, especially infected ones, pose a significant clinical challenge due to their increasing incidence and poor outcomes. The deteriorative microenvironment in such wounds, characterized by reduced extracellular matrix, impaired angiogenesis, insufficient neurogenesis, and persistent bacterial infection, has prompted the exploration of novel therapeutic strategies. In this study, we developed an injectable multifunctional hydrogel (GEL/BG@Cu + Mg) incorporating Gelatin-Tannic acid/ N-hydroxysuccinimide functionalized polyethylene glycol and Bioactive glass doped with copper and magnesium ions to accelerate the healing of infected wounds. The GEL/BG@Cu + Mg hydrogel composite demonstrates good biocompatibility, degradability, and rapid formation of a protective barrier to stop bleeding. Synergistic bactericidal effects are achieved through the photothermal properties of BG@Cu + Mg and sustained copper ions release, with the latter further promoting angiogenesis. Furthermore, the hydrogel enhances neurogenesis by stimulating axons and Schwann cells in the wound bed through the beneficial effects of magnesium ions. Our results demonstrate that the designed novel multifunctional hydrogel holds tremendous promise for treating infected wounds and allowing regenerative neurogenesis at the wound site, which provides a viable alternative for further improving clinical outcomes.

Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios.

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

ConspectusMolecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

Taurocholic acid ameliorates hypertension through the activation of TGR5 in the hypothalamic paraventricular nucleus.

Diets rich in taurine can increase the production of taurine-conjugated bile acids, which are known to exert antihypertensive effects. Despite their benefits to the heart, kidney and arteries, their role in the central nervous system during the antihypertensive process remains unclear. Since hypothalamic paraventricular nucleus (PVN) plays a key role in blood pressure regulation, we aimed to investigate the function of bile acids in the PVN. The concentration of bile acids in the PVN of spontaneously hypertensive rats (SHRs) and normotensive Wistar-Kyoto rats (WKY) fed with normal chow was measured using LC-MS/MS, which identified taurocholic acid (TCA) as the most down-regulated bile acid. To fully understand the mechanism of TCA's functions in the PVN, bi-lateral PVN micro-infusion of TCA was carried out. TCA treatment in the PVN led to a significant reduction in the blood pressure of SHRs, with decreased plasma levels of norepinephrine and improved morphology of cardiomyocytes. It also decreased the number of c-fos+ neurons, reduced the inflammatory response, and suppressed oxidative stress in the PVN of the SHRs. Most importantly, the TGR5 receptors in neurons and microglia were activated. PVN infusion of SBI-115, a TGR5 specific antagonist, was able to counteract with TCA in the blood pressure regulation of SHRs. In conclusion, TCA supplementation in the PVN of SHRs can activate TGR5 in neurons and microglia, reduce the inflammatory response and oxidative stress, suppress activated neurons, and attenuate hypertension.

The phytochemistry, pharmacology, pharmacokinetics, quality control, and toxicity of Forsythiae Fructus: An updated systematic review.

Forsythiae Fructus (FF), the dried fruit of F. suspensa, is commonly used to treat fever, inflammation, etc in China or other Asian countries. FF is usually used as the core herb in traditional Chinese medicine preparations for the treatment of influenza, such as Shuang-huang-lian oral liquid and Yin-qiao powder, etc. Since the wide application and core role of FF, its research progress was summarized in terms of traditional uses, phytochemistry, pharmacology, pharmacokinetics, quality control, and toxicity. Meanwhile, the anti-influenza substances and mechanism of FF were emphasized. Till now, a total of 290 chemical components are identified in F. suspensa, and among them, 248 components were isolated and identified from FF, including 42 phenylethanoid glycosides, 48 lignans, 59 terpenoids, 14 flavonoids, 3 steroids, 24 cyclohexyl ethanol derivatives, 14 alkaloids, 26 organic acids, and 18 other types. FF and their pure compounds have the pharmacological activities of anti-virus, anti-inflammation, anti-oxidant, anti-bacteria, anti-tumor, neuroprotection, hepatoprotection, etc. Inhibition of TLR7, RIG-I, MAVS, NF-κB, MyD88 signaling pathway were the reported anti-influenza mechanisms of FF and phenylethanoid glycosides and lignans are the main active groups. However, the bioavailability of phenylethanoid glycosides and lignans of FF in vivo was low, which needed to be improved. Simultaneously, the un-elucidated compounds and anti-influenza substances of FF strongly needed to be explored. The current quality control of FF was only about forsythoside A and phillyrin, more active components should be taken into consideration. Moreover, there are no reports of toxicity of FF yet, but the toxicity of FF should be not neglected in clinical applications.

Immunotherapy combined with targeted therapy in advanced small cell carcinoma of the ovary of hypercalcemic type: A case of overall survival lasting for over 5 years.

Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare but highly aggressive ovarian malignant neoplasm lacking a unified clinical management process. Most patients are diagnosed at an advanced stage and have an extremely poor prognosis with an overall probability of survival less than 10 %. Here, we describe the case of a patient with advanced SCCOHT achieved a survival of over 5 years after receiving multiple cycles of immunotherapy combined with anti-angiogenic therapy or CDK4/6 inhibitors. At the same time, we also summarized the case reports and clinical trials of immunotherapy in SCCOHT.

A general model for predicting enzyme functions based on enzymatic reactions.

Accurate prediction of the enzyme comission (EC) numbers for chemical reactions is essential for the understanding and manipulation of enzyme functions, biocatalytic processes and biosynthetic planning. A number of machine leanring (ML)-based models have been developed to classify enzymatic reactions, showing great advantages over costly and long-winded experimental verifications. However, the prediction accuracy for most available models trained on the records of chemical reactions without specifying the enzymatic catalysts is rather limited. In this study, we introduced BEC-Pred, a BERT-based multiclassification model, for predicting EC numbers associated with reactions. Leveraging transfer learning, our approach achieves precise forecasting across a wide variety of Enzyme Commission (EC) numbers solely through analysis of the SMILES sequences of substrates and products. BEC-Pred model outperformed other sequence and graph-based ML methods, attaining a higher accuracy of 91.6%, surpassing them by 5.5%, and exhibiting superior F1 scores with improvements of 6.6% and 6.0%, respectively. The enhanced performance highlights the potential of BEC-Pred to serve as a reliable foundational tool to accelerate the cutting-edge research in synthetic biology and drug metabolism. Moreover, we discussed a few examples on how BEC-Pred could accurately predict the enzymatic classification for the Novozym 435-induced hydrolysis and lipase efficient catalytic synthesis. We anticipate that BEC-Pred will have a positive impact on the progression of enzymatic research.