Prostate cancer microenvironment: multidimensional regulation of immune cells, vascular system, stromal cells, and microbiota.

BACKGROUND: Prostate cancer (PCa) is one of the most prevalent malignancies in males worldwide. Increasing research attention has focused on the PCa microenvironment, which plays a crucial role in tumor progression and therapy resistance. This review aims to provide a comprehensive overview of the key components of the PCa microenvironment, including immune cells, vascular systems, stromal cells, and microbiota, and explore their implications for diagnosis and treatment. METHODS: Keywords such as "prostate cancer", "tumor microenvironment", "immune cells", "vascular system", "stromal cells", and "microbiota" were used for literature retrieval through online databases including PubMed and Web of Science. Studies related to the PCa microenvironment were selected, with a particular focus on those discussing the roles of immune cells, vascular systems, stromal cells, and microbiota in the development, progression, and treatment of PCa. The selection criteria prioritized peer-reviewed articles published in the last five years, aiming to summarize and analyze the latest research advancements and clinical relevance regarding the PCa microenvironment. RESULTS: The PCa microenvironment is highly complex and dynamic, with immune cells contributing to immunosuppressive conditions, stromal cells promoting tumor growth, and microbiota potentially affecting androgen metabolism. Vascular systems support angiogenesis, which fosters tumor expansion. Understanding these components offers insight into the mechanisms driving PCa progression and opens avenues for novel therapeutic strategies targeting the tumor microenvironment. CONCLUSIONS: A deeper understanding of the PCa microenvironment is crucial for advancing diagnostic techniques and developing precision therapies. This review highlights the potential of targeting the microenvironment to improve patient outcomes, emphasizing its significance in the broader context of PCa research and treatment innovation.

Prospective Cohort Investigation on Physical Activity of Osteoporosis Outcomes (PAOPO) in Jidong: Objectives, Study Design, and Baseline Characteristics.

Objective: The aim of this study was to investigate the prospective association between physical activity (PA), independently or in conjunction with other contributing factors, and osteoporosis (OP) outcomes. Methods: The Physical Activity in Osteoporosis Outcomes (PAOPO) study was a community-based cohort investigation. A structured questionnaire was used to gather the participants' sociodemographic characteristics. Bone mineral density (BMD) measurements were performed to assess OP outcomes, and the relationship between BMD and OP was evaluated within this cohort. Results: From 2013 to 2014, 8,471 participants aged 18 years and older were recruited from Tangshan, China's Jidong community. Based on their PA level, participants were categorized as inactive, moderately active, or very active. Men showed higher physical exercise levels than women across the activity groups. BMD was significantly higher in the very active group than in the moderately active and inactive groups. Individuals aged > 50 years are at a higher risk of developing OP and osteopenia. Conclusion: The PAOPO study offers promising insights into the relationship between PA and OP outcomes, encouraging the implementation of PA in preventing and managing OP.

Six new phenolic glycosides from the roots of Scrophularia ningpoensis.

Six new phenolic glycosides (1-6) and thirteen known compounds (7-19) were isolated from the roots of Scrophularia ningpoensis. Their structures were determined through comprehensive analyses of the physicochemical properties and spectroscopic data (1D NMR, 2D NMR, and HR-ESI-MS). The anti-inflammatory potential of all compounds was evaluated by determining their ability to inhibit lipopolysaccharide (LPS) induced NO production in RAW 264.7 macrophage cells. In addition, the cytotoxicity of all compounds on HepG2, 4T1 and A549 cell lines was also evaluated.

Mechanisms underlying delirium in patients with critical illness.

Delirium is an acute, global cognitive disorder syndrome, also known as acute brain syndrome, characterized by disturbance of attention and awareness and fluctuation of symptoms. Its incidence is high among critically ill patients. Once patients develop delirium, it increases the risk of unplanned extubation, prolongs hospital stay, increases the risk of nosocomial infection, post-intensive care syndrome-cognitive impairment, and even death. Therefore, it is of great importance to understand how delirium occurs and to reduce the incidence of delirium in critically ill patients. This paper reviews the potential pathophysiological mechanisms of delirium in critically ill patients, with the aim of better understanding its pathophysiological processes, guiding the formulation of effective prevention and treatment strategies, providing a basis for clinical medication.

Can routine EPA-based assessments predict OSCE performances of undergraduate medical students?

BACKGROUND: Objective structured clinical examination (OSCE) is used worldwide. This study aims to explore potential alternatives to the OSCE by using entrustable professional activities (EPA)-based assessments in the workplace. METHODS: This study enrolled 265 six-year undergraduate medical students (UGY) from 2021 to 2023. During their rotations, students were assessed using 13 EPAs, with the grading methods modified to facilitate application. Before graduation, they participated in two mock OSCEs and a National OSCE. We used generalized estimating equations to analyze the associations between the EPA assessments and the OSCE scores, adjusting for age and sex, and developed a prediction model. EPA8 and EPA9, which represent advanced abilities that were not significant in the regression models, were removed from the prediction model. RESULTS: Most EPAs were significantly correlated with OSCE scores across the three cohorts. The prediction model for forecasting passing in the three OSCEs demonstrated fair predictive capacity (area under curve = 0.82, 0.66, and 0.71 for students graduated in 2021, 2022, and 2023, respectively all p < 0.05). CONCLUSIONS: The workplace-based assessments (EPA) showed a high correlation with competency-based assessments in simulated settings (OSCE). EPAs may serve as alternative tools to formal OSCE for medical students.

Digital transformation of mental health therapy by integrating digitalized cognitive behavioral therapy and eye movement desensitization and reprocessing.

Digital therapy has gained popularity in the mental health field because of its convenience and accessibility. One major benefit of digital therapy is its ability to address therapist shortages. Posttraumatic stress disorder (PTSD) is a debilitating mental health condition that can develop after an individual experiences or witnesses a traumatic event. Digital therapy is an important resource for individuals with PTSD who may not have access to traditional in-person therapy. Cognitive behavioral therapy (CBT) and eye movement desensitization and reprocessing (EMDR) are two evidence-based psychotherapies that have shown efficacy in treating PTSD. This paper examines the mechanisms and clinical symptoms of PTSD as well as the principles and applications of CBT and EMDR. Additionally, the potential of digital therapy, including internet-based CBT, video conferencing-based therapy, and exposure therapy using augmented and virtual reality, is explored. This paper also discusses the engineering techniques employed in digital psychotherapy, such as emotion detection models and text analysis, for assessing patients' emotional states. Furthermore, it addresses the challenges faced in digital therapy, including regulatory issues, hardware limitations, privacy and security concerns, and effectiveness considerations. Overall, this paper provides a comprehensive overview of the current state of digital psychotherapy for PTSD treatment and highlights the opportunities and challenges in this rapidly evolving field.

Association of hemoglobin glycation index with clinical outcomes in patients with coronary artery disease: a prospective cohort study.

BACKGROUND: To analyze the association between the hemoglobin glycation index (HGI) and the long-term prognosis of patients with coronary artery disease (CAD). METHODS: HGI represented the difference between laboratory measured Hemoglobin A1c (HbA1c) and predicted HbA1c based on a liner regression between Hb1Ac and fasting plasma glucose (FPG). A total of 10 598 patients who treated with percutaneous coronary intervention (PCI) were stratified into three groups (low HGI group: HGI<-0.506, medium HGI group: -0.506 ≤ HGI < 0.179, and high HGI group: HGI ≥ 0.179). The primary endpoints includes all-cause mortality (ACM) and cardiac mortality (CM). The secondary endpoints were major adverse cardiac events (MACEs) and major adverse cardiac and cerebrovascular events (MACCEs). RESULTS: A total of 321 ACMs, 243 CMs, 774 MACEs, and 854 MACCEs were recorded during a 60-month follow-up period. After adjusting for confounders using a multivariate Cox regression analysis, the patients in the low HGI group had a significantly increased risk of ACM (adjusted HR = 1.683, 95%CI:1.179-2.404, P = 0.004) and CM (HR = 1.604, 95%CI:1.064-2.417, P = 0.024) as compared with patients in the medium HGI group. Similarly, the patients in the high HGI group had an increased risk of MACEs (HR = 1.247, 95% CI: 1.023-1.521, P = 0.029) as compared with patients in the medium HGI group. For ACM, CM, and MACEs, a U-shaped relation were found among these three groups. However, we did not find significant differences in the incidence of MACCEs among these three groups. CONCLUSION: The present study indicates that HGI could be an independent predictor for the risk of mortality and MACEs in patients with CAD.

Molecular characterization of common zoonotic protozoan parasites and bacteria causing diarrhea in dairy calves in Ningxia Hui Autonomous Region, China.

Diarrhea caused by zoonotic pathogens is one of the most common diseases in dairy calves, threatening the health of young animals. Humans are also at risk, in particular children. To explore the pathogens causing diarrhea in dairy calves, the present study applied PCR-based sequencing tools to investigate the occurrence and molecular characteristics of three parasites (Cryptosporidium spp., Giardia duodenalis, and Enterocytozoon bieneusi) and three bacterial pathogens (Escherichia coli, Clostridium perfringens, and Salmonella spp.) in 343 fecal samples of diarrheic dairy calves from five farms in Lingwu County, Ningxia Hui Autonomous Region, China. The total positive rate of these pathogens in diarrheic dairy calves was 91.0% (312/343; 95% CI, 87.9-94.0), with C. perfringens (61.5%, 211/343; 95% CI, 56.3-66.7) being the dominant one. Co-infection with two to five pathogens was found in 67.3% (231/343; 95% CI, 62.4-72.3) of investigated samples. There were significant differences (p < 0.05) in the positive rates of Cryptosporidium spp. and diarrheagenic E. coli among farms, age groups, and seasons. Two Cryptosporidium species (C. parvum and C. bovis) and five gp60 subtypes of C. parvum (IIdA15G1, IIdA20G1, IIdA19G1, IIdA14G1, and a novel IIdA13G1) were identified. Two assemblages (assemblage E and zoonotic assemblage A) of G. duodenalis and six ITS genotypes of E. bieneusi (J, Henan-IV, EbpC, I, EbpA, and ESH-01) were observed. Four virulence genes (eaeA, stx1, stx2, and st) of diarrheagenic E. coli and one toxin type (type A) of C. perfringens were detected. Our study enriches our knowledge on the characteristics and zoonotic potential of diarrhea-related pathogens in dairy calves.

Title: Caractérisation moléculaire des protozoaires parasites zoonotiques courants et des bactéries responsables de diarrhée chez les veaux laitiers dans la région autonome Hui du Ningxia, en Chine. Abstract: La diarrhée causée par des agents pathogènes zoonotiques est l'une des maladies les plus courantes chez les veaux laitiers, menaçant la santé des jeunes animaux. Ceci est également un risque pour la santé humaine, en particulier les enfants. Pour explorer les agents pathogènes responsables de la diarrhée chez les veaux laitiers, cette étude a utilisé des outils de séquençage basés sur la PCR pour étudier l'occurrence et les caractères moléculaires de trois parasites (Cryptosporidium spp., Giardia duodenalis et Enterocytozoon bieneusi) et de trois agents pathogènes bactériens (Escherichia coli, Clostridium perfringens et Salmonella spp.) dans 343 échantillons fécaux de veaux laitiers diarrhéiques provenant de cinq fermes du comté de Lingwu, région autonome Hui du Ningxia, en Chine. Le taux total positif de ces pathogènes chez les veaux laitiers diarrhéiques était de 91,0 % (312/343; IC à 95 %, 87,9­94,0), et C. perfringens (61,5 %, 211/343; IC à 95 %, 56,3­66,7) était le plus répandu. Une co-infection avec deux à cinq pathogènes a été trouvée dans 67,3 % (231/343; IC à 95 %, 62,4­72,3) des échantillons étudiés. Il y avait des différences significatives (p < 0,05) dans les taux positifs de Cryptosporidium spp. et d'E. coli diarrhéogènes entre les fermes, les groupes d'âge et les saisons. Deux espèces de Cryptosporidium (C. parvum et C. bovis) et cinq sous-types de gp60 de C. parvum (IIdA15G1, IIdA20G1, IIdA19G1, IIdA14G1 et un nouveau, IIdA13G1) ont été identifiés. Deux assemblages (assemblage E et assemblage zoonotique A) de G. duodenalis et six génotypes ITS d'E. bieneusi (J, Henan-IV, EbpC, I, EbpA et ESH-01) ont été observés. Quatre gènes de virulence (eaeA, stx1, stx2 et st) d'E. coli diarrhéogènes et un type de toxine (type A) de C. perfringens ont été détectés. Notre étude enrichit les connaissances sur les caractères et le potentiel zoonotique des agents pathogènes liés à la diarrhée chez les veaux laitiers.

Construction and evaluation of a liver cancer risk prediction model based on machine learning.

BACKGROUND: Liver cancer is one of the most prevalent malignant tumors worldwide, and its early detection and treatment are crucial for enhancing patient survival rates and quality of life. However, the early symptoms of liver cancer are often not obvious, resulting in a late-stage diagnosis in many patients, which significantly reduces the effectiveness of treatment. Developing a highly targeted, widely applicable, and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals. AIM: To develop a liver cancer risk prediction model by employing machine learning techniques, and subsequently assess its performance. METHODS: In this study, a total of 550 patients were enrolled, with 190 hepatocellular carcinoma (HCC) and 195 cirrhosis patients serving as the training cohort, and 83 HCC and 82 cirrhosis patients forming the validation cohort. Logistic regression (LR), support vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO) regression models were developed in the training cohort. Model performance was assessed in the validation cohort. Additionally, this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) to determine the optimal predictive model for assessing liver cancer risk. RESULTS: Six variables including age, white blood cell, red blood cell, platelet counts, alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR, SVM, RF, and LASSO regression models. The RF model exhibited superior discrimination, and the area under curve of the training and validation sets was 0.969 and 0.858, respectively. These values significantly surpassed those of the LR (0.850 and 0.827), SVM (0.860 and 0.803), LASSO regression (0.845 and 0.831), and ASAP (0.866 and 0.813) models. Furthermore, calibration and DCA indicated that the RF model exhibited robust calibration and clinical validity. CONCLUSION: The RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.

Mental Health Literacy Among Urban and Rural Residents of Guangdong Province, China.

Purpose: The study aims to understanding the mental health literacy level of urban and rural residents in Guangdong Province, the first major province in China, and its influencing factors is crucial. Methods: A multi-stage stratified equal-volume random sampling method was adopted in October-December 2022 to select permanent residents aged 18 years and above in Guangdong Province for the questionnaire survey, which consisted of a general demographic information questionnaire and a national mental health literacy questionnaire. Rao-Scott χ²-test with correction based on sampling design, independent samples t-test and binary multivariate logistic regression analysis were performed. Results: A total of 51744 individuals completed the questionnaire, including 31822 urban residents and 19200 rural residents. The rate of achievement of mental health literacy was 13.6% among urban residents, which was significantly higher compared to the rate of 8.6% among rural residents. Logistic regression analysis showed that female, higher education, being mental worker, being a retiree, having a higher monthly household income, maintaining a regular diet, and using electronic products for 2-6 hours per day were protective factors for mental health literacy attainment in urban residents, while having chronic diseases, being a smoker and having a history of drinking were identified as risk factors in urban residents. Among in rural residents, married, younger, higher education, being mental worker and using electronic products for 2-6 hours per day, maintaining a regular diet, and engaging in regular exercise were protective factors for achieving mental health literacy, while previous smoking was a risk factor. Conclusion: The study revealed a low level of mental health literacy among urban and rural residents of Guangdong Province, with a significant disparity between the two areas. These findings highlight the need for continuing efforts to increase the dissemination of mental health knowledge in rural communities and improve levels of mental health literacy.

Synthesis of ß-ionone from xylose and lignocellulosic hydrolysate in genetically engineered oleaginous yeast Yarrowia lipolytica.

ß-ionone, an apocarotenoid derived from a C40 terpenoid has an intense, woody smell and a low odor threshold that has been widely used in as an ingredient in food and cosmetics. Yarrowia lipolytica is a promising host for ß-ionone production because of its oleaginous nature, its ability to produce high levels of acetyl-CoA (an important precursor for terpenoids), and the availability of synthetic biology tools to engineer the organism. In this study, ß-carotene-producing Y. lipolytica strain XK17 was employed for ß-ionone biosynthesis. First, we explored the effect of different sources of carotenoid cleavage dioxygenase (CCD) genes on ß-ionone production. A high-yielding strain rUinO-D14 with 122 mg/L of ß-ionone was obtained by screening promoters combined with rDNA mediated multi-round iterative transformations to optimize the expression of the CCD gene of Osmanthus fragrans. Second, to further develop a high-level production strain for ß-ionone, we optimized key genes in the mevalonate pathway by multi-round iterative transformations mediated by non-homologous end joining, combined with a protein tagging strategy. Finally, the introduction of a heterologous oxidoreductase pathway enabled the engineered Y. lipolytica strain to use xylose as a sole carbon source and produce ß-ionone. In addition, the potential for use of lignocellulosic hydrolysate as the carbon source for ß-ionone production showed that the NHA-A31 strain had a high ß-ionone productivity level. This study demonstrates that engineered Y. lipolytica can be used for the efficient, green and sustainable production of ß-ionone.

Smoking in relation to early dental implant failure: A systematic review and meta-analysis.

OBJECTIVE: To assess the association between smoking and early dental implant failure by conducting a systematic review and meta-analysis of observational studies. DATA SOURCES: PubMed, Embase, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials were systematically searched for reports of relevant studies addressing the relationship between smoking and early dental implant failure published between database inception and June 2024. STUDY SELECTION: Thirty-two observational clinical studies published between 1994 and 2024 were included, with a total of 59,246 implants at implant level and 14,115 patients at individual level. At implant level, a meta-analysis of 21 included cohort studies showed that smoking was associated with increased risk of early dental implant failure compared with non-smoking (odds ratio [OR], 2.59; 95 % confidence interval [CI], 2.08-3.23). Three included studies reported that smoking was associated with higher maxillary early dental implant failure risk (OR, 5.90; 95 %CI, 2.38-14.66) than that of mandible (OR, 3.76; 95 %CI, 1.19-11.87). At individual level, meta-analysis of thirty cohort studies indicated that risk of early implant failure in smokers was 100 % higher than in non-smokers (OR, 2.00; 95 %CI, 1.43-2.80). Three case-control studies found that risk of early implant failure of smokers was 59 % higher than that of non-smokers (OR, 1.59; 95 %CI, 1.28-1.97). CONCLUSIONS: Smoking was significantly associated with early dental implant failure, particularly at the maxillary location, at both implant and individual level. These findings suggest smoking cessation is a crucial factor in reducing risk of early dental implant failure. CLINICAL SIGNIFICANCE: There is uncertainty about the extent to which smoking influences early dental implant failure, our meta-analysis of findings emphasize smoking was significantly associated with early dental implant failure, particularly at the maxillary location.

Metal-organic frameworks with two different-sized aromatic ring-confined nanotraps for benchmark natural gas upgrade.

Recovery of light alkanes from natural gas is of great significance in petrochemical production. Herein, a promising strategy utilizing two types of size-complementary aromatic ring-confined nanotraps (called bi-nanotraps here) is proposed to efficiently trap ethane (C2H6) and propane (C3H8) selectively at their respective sites. Two isostructural metal-organic frameworks (MOFs, SNNU-185/186), each containing bi-nanotraps decorated with six aromatic rings, are selected to demonstrate the feasibility of this method. The smaller nanotrap acts as adsorption sites tailored for C2H6 while the larger one is optimized in size for C3H8. The separation is further facilitated by the large channels, which serve as mass transfer pathways. These advanced features give rise to multiple C-H⋯π interactions and size/shape-selective interaction sites, enabling SNNU-185/186 to achieve high C2H6 adsorption enthalpy (43.5/48.8 kJ mol-1) and a very large thermodynamic interaction difference between C2H6 and CH4. Benefiting from the bi-nanotrap effect, SNNU-185/186 exhibits benchmark experimental natural gas upgrade performance with top-level CH4 productivity (6.85/6.10 mmol g-1), ultra-high purity and first-class capture capacity for C2H6 (1.23/0.90 mmol g-1) and C3H8 (2.33/2.15 mmol g-1).

Associations between peripheral whole blood cell counts derived indexes and cancer prognosis: An umbrella review of meta-analyses of cohort studies.

Meta-analyses have reported conflicting data on the whole blood cell count (WBCC) derived indexes (neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], and lymphocyte-to-monocyte ratio [LMR]) and cancer prognosis. However, the strength and quality of this evidence has not been quantified in aggregate. To grade the evidence from published meta-analyses of cohort studies that investigated the associations between NLR, PLR, and LMR and cancer prognosis. A total of 694 associations from 224 articles were included. And 219 (97.8%) articles rated as moderate-to-high quality according to AMSTAR. There were four associations supported by convincing evidence. Meanwhile, 165 and 164 associations were supported by highly suggestive and suggestive evidence, respectively. In this umbrella review, we summarized the existing evidence on the WBCC-derived indexes and cancer prognosis. Due to the direction of effect sizes is not completely consistent between studies, further research is needed to assess causality and provide firm evidence.

Prognostic Value of Fibrosis-4 in Acute Ischemic Stroke Patients Undergoing Intravenous Thrombolysis.

Purpose: Although recombinant tissue plasminogen activator (rt-PA) treatment is efficient in patients with acute ischemic stroke (AIS), a significant percentage of patients who received rt-PA intravenous thrombolysis (IVT) do not achieve a good prognosis. Therefore, the factors that affect the poor prognosis of patients with IVT are needed. The Fibrosis-4 (FIB-4) index has been used as a liver fibrosis biomarker. We aimed to investigate the relationship between the FIB-4 index and functional outcomes in patients with AIS receiving IVT. Patients and Methods: This study prospectively included consecutive patients with AIS receiving IVT between April 2015 and May 2022. We collected clinical and laboratory data and calculated the FIB-4 index. Clinical outcome was poor functional outcome (mRS ≥3) at 3 months after IVT. Multivariate logistic regression analysis was used to analyze the association between FIB-4 and outcome. We explored the interactive effect of FIB-4 and dyslipidemia on poor outcomes, and subgroup analysis was performed. Furthermore, an individualized prediction model based on the FIB-4 for functional outcome was established in the dyslipidemia group. Results: A total of 1135 patients were included, and 41.50% had poor 3-month outcomes. After adjusted by other variants that P value <0.05 in univariable analysis, FIB-4 was independently associated with poor outcomes (OR=1.420; 95% CI: 1.113-1.812; P=0.004). There was a significant interaction between FIB-4 and dyslipidemia on poor outcome (P=0.036), and the independent association between FIB-4 and poor outcome was maintained in the dyslipidemia subgroup (OR=1.646; 95% CI: 1.228-2.206; P=0.001). Furthermore, in the dyslipidemia group, the FIB-4-based prediction model had good predictive value (the AUC of the training and validation sets were 0.767 and 0.708, respectively), good calibration (P-values for the Hosmer-Lemeshow test >0.05), and clinical usefulness. Conclusion: FIB-4 is an independent risk factor for poor outcomes in IVT patients with dyslipidemia, which can be used as a simple predictor of their prognosis.

Palladium-Catalyzed Cyclization and Direct Arylation of 1-[2-(2,2-Dibromoethenyl)phenyl]-1H-pyrrole with Allenes.

A novel and efficient palladium-catalyzed highly regioselective reaction of 1-[2-(2,2-dibromoethenyl)phenyl]-1H-pyrrole with allenes was realized to synthesize pyrrolo[1,2-a]quinolones. The tandem process involves intermolecular cyclization and intramolecular direct arylation, leading to the formation three new C-C bonds and two new rings. Notably, this transformation exhibits broad substrate scope and high functional group tolerance.

First reported advanced pancreatic cancer with hyperprogression treated with PD-1 blockade combined with chemotherapy: a case report and literature review.

Pancreatic cancer is among the most immune-resistant tumor types due to its unique tumor microenvironment and low cancer immunogenicity. Single-agent immune modulators have thus far proven clinically ineffective. However, a growing body of evidence suggests that combination of these modulators with other strategies could unlock the potential of immunotherapy in pancreatic cancer. Herein, we describe the case of a 59-year-old male with metastatic pancreatic ductal adenocarcinoma, referred to our center to receive immunotherapy (serplulimab, a novel anti-PD-1 antibody) combined with chemotherapy (gemcitabine/nab-paclitaxel). During the initial three treatment cycles, the patient was assessed as having stable disease (SD) according to RECIST 1.1 criteria. However, following two additional cycles of combination therapy, the primary tumor mass increased from 4.9 cm to 13.2 cm, accompanied by the development of new lung lesions, ascites, and pelvic metastases. He succumbed to respiratory failure one month later. Retrospective analysis revealed that the patient had MDM4 amplification, identified as a high-risk factor for hyperprogressive disease (HPD). To our knowledge, this is the first reported case of HPD in pancreatic cancer with multiple metastases treated using combination therapy. We investigated the potential mechanisms and reviewed the latest literature on predictive factors for HPD. These findings suggest that while chemotherapy combined with immunotherapy may hold promise for treating pancreatic cancer, it is imperative to identify and closely monitor patients with high-risk factors for HPD when using immunotherapy.

An intramolecular charge transfer based fluorescent probe for imaging of OCl.

The discovery and utilization of new fluorescent chromophore is indispensable to exploit high performance probes for biological research. Stokes shift is one of the most important properties of chromophore accounting for super-resolution fluorescence imaging. Intramolecular charge transfer (ICT) is one of the fundamental mechanisms for fluorescence that accompanied by large Stokes shifts. Based on the conformational changes between ground and excited states, ICT models can be divided into two types: conformation-steady ICT, whose conformation remains unchanged, and conformation-changeable ICT, which is characterized by the rotation of the chromophore around an axis upon excitation. Herein, we report a new chromophore whose donor and acceptor parts took a butterfly geometry with a dihedral angle of 21° in ground state and a planar conformation upon photo excitation. The bent conformation might be ascribed to the extra conjugated double bond, which made the coplanarity of the chromophore in ground state get worse. The chromophore shows a remarkable Stokes shift over 150 nm and a high fluorescence quantum yieldof 0.62. The limit of detection is 41 nM, which enabled the imaging of basal as well as induced OCl- in different cells. Moreover, the pronounced spectroscopic properties ensure the in vivo monitoring of OCl- in arthritic mice. This finding would shed light on the exploitation of small molecule probes based on new fluorescence chromophore for precise biological imaging.

Machine learning applications in breast cancer survival and therapeutic outcome prediction based on multi-omic analysis.

The high heterogeneity within and between breast cancer patients complicates treatment determination and prognosis assessment. Treatment decision-making is influenced by various factors, such as tumor subtype, histological grade, and genotype, necessitating personalized treatment strategies. Prognostic outcomes vary significantly depending on patient-specific conditions. As a critical branch of artificial intelligence, machine learning efficiently handles large datasets and automates decision-making processes. The introduction of machine learning offers new solutions for breast cancer treatment selection and prognosis assessment. In the field of cancer therapy, traditional methods for predicting treatment and survival outcomes often rely on single or few biomarkers, limiting their ability to capture the complexity of biological processes comprehensively. Machine learning analyzes patients' multi-omic data and the intricate patterns of variations during cancer initiation and progression to predict patients' survival and treatment outcomes. Consequently, it facilitates the selection of appropriate therapeutic interventions to implement early intervention and improve treatment efficacy for patients. Here, we first introduce common machine learning methods, and then elaborate on the application of machine learning in the field of survival prediction and prognosis from two aspects: evaluating survival and predicting treatment outcomes for breast cancer patients. The aim is to provide breast cancer patients with precise treatment strategies to improve therapeutic outcomes and quality of life.