Clinical implications of CT-detected ascites in gastric cancer: association with peritoneal metastasis and systemic inflammatory response.

OBJECTIVES: This study aimed to evaluate the diagnostic significance of computed tomography (CT) detected ascites in gastric cancer (GC) with peritoneal metastasis (PM) and investigate its association with systemic inflammatory response. METHODS: This retrospective study included 111 GCs with ascites (PM: n = 51; No PM: n = 60). Systemic inflammatory indexes, tumor markers, and the CT-assessed characteristics of ascites were collected. The differences in parameters between the two groups were analyzed. Diagnostic performance was obtained by receiver operating characteristic curve analysis. The association between the volume of ascites and clinical characteristics was evaluated with correlation analysis. RESULTS: In this study, over half of GCs with ascites were not involved with PM. The systemic immune-inflammation index (SII), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), five tumor markers, and the characteristics of ascites showed significant differences between the two groups (all p < 0.05). Among them, SII, NLR, PLR, and the volume of ascites achieved the areas under the curve of 0.700, 0.698, 0.704, and 0.903, respectively. Moreover, the volumes of ascites showed positive correlations with SII, NLR, and PLR in GCs with PM, and the volumes of ascites detected in the upper abdomen were more strongly correlated with CA125 level (all p < 0.05). CONCLUSION: Many GCs with CT-detected ascites did not occur with synchronous PM. The presence of upper abdominal ascites had certain clinical significance for diagnosing PM in GCs. Systemic inflammatory indexes were elevated and positively correlated with the volume of ascites in GCs with PM, which might suggest the enhanced systemic inflammatory response. CRITICAL RELEVANCE STATEMENT: CT-detected ascites in the upper abdomen played an indicative role in identifying synchronous PM in GCs, and the systemic inflammatory response was enhanced in GCs with PM, which might be helpful for clinical evaluation. KEY POINTS: Many GCs with CT-detected ascites did not occur with synchronous PM. CT-detected ascites in the upper abdomen help in identifying PM in GCs. GCs with PM showed elevated systemic inflammatory indexes and enhanced systemic inflammatory response.

Generation of human expandable limb-bud-like progenitors via chemically induced dedifferentiation.

In certain highly regenerative animals, cellular dedifferentiation occurs after injury, allowing specialized cells to become progenitor cells for regeneration. However, this capacity is restricted in human cells due to reduced plasticity. Here, we introduce a chemical-induced dedifferentiation approach that reverts the differentiated cells to a progenitor-like state, conferring the features of human limb bud cells from human adult somatic cells. These chemically induced human limb-bud-like progenitors (hCiLBP cells) show a high degree of transcriptomic similarity to human embryonic limb bud progenitors. Importantly, we established culture conditions that allow hCiLBP cells to undergo extensive expansion while maintaining population homogeneity and long-term self-renewal capacity. Moreover, hCiLBP cells exhibit increased osteochondrogenic differentiation ability, providing an innovative platform for generation of skeletal lineage cell types. These results highlight a potential therapeutic approach for repairing damaged human tissues through reversal of developmental pathways from mature cells to expandable progenitor cells.

Generative Large Language Models in Electronic Health Records for Patient Care Since 2023: A Systematic Review.

Background: Generative Large language models (LLMs) represent a significant advancement in natural language processing, achieving state-of-the-art performance across various tasks. However, their application in clinical settings using real electronic health records (EHRs) is still rare and presents numerous challenges. Objective: This study aims to systematically review the use of generative LLMs, and the effectiveness of relevant techniques in patient care-related topics involving EHRs, summarize the challenges faced, and suggest future directions. Methods: A Boolean search for peer-reviewed articles was conducted on May 19th, 2024 using PubMed and Web of Science to include research articles published since 2023, which was one month after the release of ChatGPT. The search results were deduplicated. Multiple reviewers, including biomedical informaticians, computer scientists, and a physician, screened the publications for eligibility and conducted data extraction. Only studies utilizing generative LLMs to analyze real EHR data were included. We summarized the use of prompt engineering, fine-tuning, multimodal EHR data, and evaluation matrices. Additionally, we identified current challenges in applying LLMs in clinical settings as reported by the included studies and proposed future directions. Results: The initial search identified 6,328 unique studies, with 76 studies included after eligibility screening. Of these, 67 studies (88.2%) employed zero-shot prompting, five of them reported 100% accuracy on five specific clinical tasks. Nine studies used advanced prompting strategies; four tested these strategies experimentally, finding that prompt engineering improved performance, with one study noting a non-linear relationship between the number of examples in a prompt and performance improvement. Eight studies explored fine-tuning generative LLMs, all reported performance improvements on specific tasks, but three of them noted potential performance degradation after fine-tuning on certain tasks. Only two studies utilized multimodal data, which improved LLM-based decision-making and enabled accurate rare disease diagnosis and prognosis. The studies employed 55 different evaluation metrics for 22 purposes, such as correctness, completeness, and conciseness. Two studies investigated LLM bias, with one detecting no bias and the other finding that male patients received more appropriate clinical decision-making suggestions. Six studies identified hallucinations, such as fabricating patient names in structured thyroid ultrasound reports. Additional challenges included but were not limited to the impersonal tone of LLM consultations, which made patients uncomfortable, and the difficulty patients had in understanding LLM responses. Conclusion: Our review indicates that few studies have employed advanced computational techniques to enhance LLM performance. The diverse evaluation metrics used highlight the need for standardization. LLMs currently cannot replace physicians due to challenges such as bias, hallucinations, and impersonal responses.

An Efficient Boron Source Activation Strategy for the Low-Temperature Synthesis of Boron Nitride Nanotubes.

Lowering the synthesis temperature of boron nitride nanotubes (BNNTs) is crucial for their development. The primary reason for adopting a high temperature is to enable the effective activation of high-melting-point solid boron. In this study, we developed a novel approach for efficiently activating boron by introducing alkali metal compounds into the conventional MgO-B system. This approach can be adopted to form various low-melting-point AM-Mg-B-O growth systems. These growth systems have improved catalytic capability and reactivity even under low-temperature conditions, facilitating the synthesis of BNNTs at temperatures as low as 850 °C. In addition, molecular dynamics simulations based on density functional theory theoretically demonstrate that the systems maintain a liquid state at low temperatures and interact with N atoms to form BN chains. These findings offer novel insights into the design of boron activation and are expected to facilitate research on the low-temperature synthesis of BNNTs.

Exploring the value of multiple preprocessors and classifiers in constructing models for predicting microsatellite instability status in colorectal cancer.

Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min-Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635-0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC.

A spatiotemporal comparative analysis on tumor immune microenvironment characteristics between neoadjuvant chemotherapy and preoperative immunotherapy for ESCC.

The average five-year survival rate for esophageal cancer, a common malignant tumor of the digestive system, is barely 20%. The majority of esophageal squamous cell carcinoma (ESCC) patients had already progressed to a locally advanced or even advanced stage at initial diagnosis, making routine surgery ineffective. Chemotherapy and immunotherapy are important neoadjuvant treatments for ESCC, however, it remains unknown how treatment will affect the immunological microenvironment, especially at the spatial level. Here, we presented the TME characters of ESCC from the temporal and spatial dimensions using scRNA-seq and ST, investigated the changes of immune cell clusters in the TME under neoadjuvant chemotherapy and preoperative immunotherapy, and explored the potential mechanisms. It was found that compared with chemotherapy, immunotherapy combined with chemotherapy increased the level of T cell proliferation, partially restored the function of exhausted T cells, induced the expansion of specific exhausted CD8 T cells, increased the production of dendritic cells (DCs), and supported the immune hot microenvironment of the tumor. We also found that CD52 and ID3 have potential as biomarkers of ESCC. Particularly, CD52 may be served as a predictor of the efficacy to screen the advantaged population of different regimens. Through multiple pathways, CAF2 and CAF5's antigen-presenting role affected the other fibroblast clusters, resulting in malignant transformation. We analyzed the immune microenvironment differences between the two regimens to provide a more thorough description of the ESCC microenvironment profile and serve as a foundation for customized neoadjuvant treatment of ESCC.

Generation of dual-attribute iTNK cells from hPSCs for cancer immunotherapy.

Dual-attribute immune cells possess advantageous features of cytotoxic T cells and natural killer (NK) cells and hold promise for advancing immunotherapy. Dual-attribute cell types such as invariant natural killer T cells, induced T-to-NK cells, and cytokine-induced killer cells have demonstrated efficacy and safety in preclinical and clinical studies. However, their limited availability hinders their widespread application. Human pluripotent stem cells (hPSCs) offer an ideal source. Here, we generate dual-attribute induced T-NK (iTNK) cells from hPSCs, expressing markers of both cytotoxic T and NK cells. Single-cell RNA and T cell receptor (TCR) sequencing analyses reveal that iTNK cells expressed signature genes associated with both NK and T cells and displayed a diverse TCR repertoire. iTNK cells release cytotoxic mediators, exert cytotoxicity against diverse tumor cell lines, and inhibit tumor growth in vivo. By harnessing adaptive and innate immune responses, hPSC-derived iTNK cells offer promising strategies for cancer immunotherapy.

A simulation study of the performance of statistical models for count outcomes with excessive zeros.

BACKGROUND: Outcome measures that are count variables with excessive zeros are common in health behaviors research. Examples include the number of standard drinks consumed or alcohol-related problems experienced over time. There is a lack of empirical data about the relative performance of prevailing statistical models for assessing the efficacy of interventions when outcomes are zero-inflated, particularly compared with recently developed marginalized count regression approaches for such data. METHODS: The current simulation study examined five commonly used approaches for analyzing count outcomes, including two linear models (with outcomes on raw and log-transformed scales, respectively) and three prevailing count distribution-based models (ie, Poisson, negative binomial, and zero-inflated Poisson (ZIP) models). We also considered the marginalized zero-inflated Poisson (MZIP) model, a novel alternative that estimates the overall effects on the population mean while adjusting for zero-inflation. Motivated by alcohol misuse prevention trials, extensive simulations were conducted to evaluate and compare the statistical power and Type I error rate of the statistical models and approaches across data conditions that varied in sample size ( N = 100 $$ N=100 $$ to 500), zero rate (0.2 to 0.8), and intervention effect sizes. RESULTS: Under zero-inflation, the Poisson model failed to control the Type I error rate, resulting in higher than expected false positive results. When the intervention effects on the zero (vs. non-zero) and count parts were in the same direction, the MZIP model had the highest statistical power, followed by the linear model with outcomes on the raw scale, negative binomial model, and ZIP model. The performance of the linear model with a log-transformed outcome variable was unsatisfactory. CONCLUSIONS: The MZIP model demonstrated better statistical properties in detecting true intervention effects and controlling false positive results for zero-inflated count outcomes. This MZIP model may serve as an appealing analytical approach to evaluating overall intervention effects in studies with count outcomes marked by excessive zeros.

Exciton-harvesting enabled efficient charged particle detection in zero-dimensional halides.

Materials for radiation detection are critically important and urgently demanded in diverse fields, starting from fundamental scientific research to medical diagnostics, homeland security, and environmental monitoring. Low-dimensional halides (LDHs) exhibiting efficient self-trapped exciton (STE) emission with high photoluminescence quantum yield (PLQY) have recently shown a great potential as scintillators. However, an overlooked issue of exciton-exciton interaction in LDHs under ionizing radiation hinders the broadening of its radiation detection applications. Here, we demonstrate an exceptional enhancement of exciton-harvesting efficiency in zero-dimensional (0D) Cs3Cu2I5:Tl halide single crystals by forming strongly localized Tl-bound excitons. Because of the suppression of non-radiative exciton-exciton interaction, an excellent α/ß pulse-shape-discrimination (PSD) figure-of-merit (FoM) factor of 2.64, a superior rejection ratio of 10-9, and a high scintillation yield of 26 000 photons MeV-1 under 5.49 MeV α-ray are achieved in Cs3Cu2I5:Tl single crystals, outperforming the commercial ZnS:Ag/PVT composites for charged particle detection applications. Furthermore, a radiation detector prototype based on Cs3Cu2I5:Tl single crystal demonstrates the capability of identifying radioactive 220Rn gas for environmental radiation monitoring applications. We believe that the exciton-harvesting strategy proposed here can greatly boost the applications of LDHs materials.

TOX: a potential new immune checkpoint in cancers by pancancer analysis.

BACKGROUND: Thymocyte selection-associated HMG-BOX (TOX) belongs to a family of transcription factors containing a highly conserved region of the high mobility group box (HMG-Box). A growing body of research has shown that TOX is involved in the occurrence and development of tumors and promotes T-cell exhaustion. We assessed the role of TOX with The Cancer Genome Atlas (TCGA) Pancancer Data. METHODS: TOX expression was examined with RNA-seq data from the TCGA and Genotype-Tissue Expression (GTEx) databases. The genetic alteration status and protein level of TOX were analyzed using databases, including the Human Protein Atlas (HPA), GeneCards, and STRING. The prognostic significance was estimated with survival data from the TCGA. Moreover, R software was used for enrichment analysis of TOX. The relationship between TOX and immune cell infiltration was assessed with the Tumor Immune Estimation Resource (TIMER) 2.0 database and the "CIBERSORT" method. The correlation between TOX and immune checkpoints was further explored. Immunohistochemical analysis was used to further verify the difference in TOX expression between cancerous and paracancerous tissues, and cell viability was evaluated using a CCK-8 assay. RESULTS: In most cancer types in the TCGA cohort, differential TOX expression was observed. The genetic alteration status and protein level of TOX were examined, and the prognosis of cancers was associated with TOX expression. Moreover, TOX levels were closely related to different immune-related pathways, immune cell infiltration and immune checkpoints. Additionally, significant differences in TOX expression between several cancerous and paracancerous tissues were validated. Furthermore, TOX clearly impacted the viability of cancer cells. CONCLUSIONS: TOX, a potential biomarker for cancer, may be involved in the regulation of the immune microenvironment and can be used for new targeted drugs.

Reconstituted HDL ameliorated renal injury of diabetic kidney disease in mice.

Diabetic kidney disease (DKD) is a devastating kidney disease and lacks effective therapeutic interventions. The present study was aimed to determine whether reconstituted high-density lipoprotein (rHDL) ameliorated renal injury in eNOS-/- dbdb mice, a mouse model of DKD. Three groups of mice, wild type C57BLKS/J (non-diabetes), eNOS-/- dbdb (diabetes), and eNOS-/- dbdb treated with rHDL (diabetes+rHDL) with both males and females were used. The rHDL nanoparticles were administered to eNOS-/- dbdb mice at Week 16 at 5 µg/g body weight in ~100 µL of saline solution twice per week for 4 weeks via retroorbital injection. We found that rHDL treatment significantly blunted progression of albuminuria and GFR decline observed in DKD mice. Histological examinations showed that the rHDLs significantly alleviated glomerular injury and renal fibrosis, and inhibited podocyte loss. Western blots and immunohistochemical examinations showed that increased protein abundances of fibronectin and collagen IV in the renal cortex of eNOS-/- dbdb mice were significantly reduced by the rHDLs. Taken together, the present study suggests a renoprotective effect of rHDLs on DKD.

Synthesis, Structure, and Magnetic Properties of Cyanurates RE5(C3N3O3)(OH)12 (RE = Gd-Lu): Cryogenic Magnetocaloric Candidate Gd5(C3N3O3)(OH)12.

Rare-earth (RE)-based frustrated magnets are fertile playgrounds for discovering exotic quantum phenomena and exploring adiabatic demagnetization refrigeration applications. Here, we report the synthesis, structure, and magnetic properties of a family of rare-earth cyanurates RE5(C3N3O3)(OH)12 (RE = Gd-Lu) with an acentric space group P6̅2m. Magnetic susceptibility χ(T) and isothermal magnetization M(H) measurements manifest that RE5(C3N3O3)(OH)12 (RE = Gd, Dy-Yb) compounds exhibit no magnetic ordering down to 2 K, while Tb5(C3N3O3)(OH)12 shows long-range magnetic ordering around 3.6 K. Among them, magnetically frustrated spin-7/2 Gd5(C3N3O3)(OH)12 shows long-range magnetic ordering around 1.25 K and a large magnetocaloric effect with a maximum magnetic entropy change ΔSm of up to 58.1 J kg-1 K-1 at ΔH = 7 T at liquid-helium temperature regimes.

van der Waals Stacking-Determined Polymorphs of Quasi-One-Dimensional BiSCl Grown by Chemical Vapor Deposition.

We report chemical vapor deposition (CVD) synthesis of two quasi-one-dimensional (quasi-1D) polymorphs of BiSCl, denoted by y-BiSCl and r-BiSCl. The length of the CVD samples can reach about 0.4 mm. Such quasi-1D samples of the two polymorphs can be readily separated into individual pieces for either characterization or application. The two polymorphs can be clearly differentiated by Raman spectroscopy. First-principles calculations and group analysis are used to assign each Raman peak to the corresponding vibrational mode. Ultraviolet-visible measurements on solution grown thin-film samples reveal that the two polymorphs exhibit significantly different band gaps of 2.08 eV (y-BiSCl) and 1.81 eV (r-BiSCl). First-principles calculation further shows that the interatomic chain binding energy is 18.1 meV/Å2, confirming that the van der Waals stacking determines the difference in their band gaps. Our findings highlight the possibility of realizing the desired functionalities in quasi-1D materials by controlling stacking orientation.

Ductile P-Type AgCu(Se,S,Te) Thermoelectric Materials.

Ductile inorganic thermoelectric (TE) materials open a new approach to develop high-performance flexible TE devices. N-type Ag2(S,Se,Te) and p-type AgCu(Se,S,Te) pseudoternary solid solutions are two typical categories of ductile inorganic TE materials reported so far. Comparing with the Ag2(S,Se,Te) pseudoternary solid solutions, the phase composition, crystal structure, and physical properties of AgCu(Se,S,Te) pseudoternary solid solutions are more complex, but their relationships are still ambiguous now. In this work, via systematically investigating the phase composition, crystal structure, mechanical, and TE properties of about 60 AgCu(Se,S,Te) pseudoternary solid solutions, the comprehensive composition-structure-property phase diagrams of the AgCuSe-AgCuS-AgCuTe pseudoternary system is constructed. By mapping the complex phases, the "ductile-brittle" and "n-p" transition boundaries are determined and the composition ranges with high TE performance and inherent ductility are illustrated. On this basis, high performance p-type ductile TE materials are obtained, with a maximum zT of 0.81 at 340 K. Finally, flexible in-plane TE devices are prepared by using the AgCu(Se,S,Te)-based ductile TE materials, showing high output performance that is superior to those of organic and inorganic-organic hybrid flexible devices.

Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases.

OBJECTIVE: Existing approaches to fairness evaluation often overlook systematic differences in the social determinants of health, like demographics and socioeconomics, among comparison groups, potentially leading to inaccurate or even contradictory conclusions. This study aims to evaluate racial disparities in predicting mortality among patients with chronic diseases using a fairness detection method that considers systematic differences. METHODS: We created five datasets from Mass General Brigham's electronic health records (EHR), each focusing on a different chronic condition: congestive heart failure (CHF), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), chronic liver disease (CLD), and dementia. For each dataset, we developed separate machine learning models to predict 1-year mortality and examined racial disparities by comparing prediction performances between Black and White individuals. We compared racial fairness evaluation between the overall Black and White individuals versus their counterparts who were Black and matched White individuals identified by propensity score matching, where the systematic differences were mitigated. RESULTS: We identified significant differences between Black and White individuals in age, gender, marital status, education level, smoking status, health insurance type, body mass index, and Charlson comorbidity index (p-value < 0.001). When examining matched Black and White subpopulations identified through propensity score matching, significant differences between particular covariates existed. We observed weaker significance levels in the CHF cohort for insurance type (p = 0.043), in the CKD cohort for insurance type (p = 0.005) and education level (p = 0.016), and in the dementia cohort for body mass index (p = 0.041); with no significant differences for other covariates. When examining mortality prediction models across the five study cohorts, we conducted a comparison of fairness evaluations before and after mitigating systematic differences. We revealed significant differences in the CHF cohort with p-values of 0.021 and 0.001 in terms of F1 measure and Sensitivity for the AdaBoost model, and p-values of 0.014 and 0.003 in terms of F1 measure and Sensitivity for the MLP model, respectively. DISCUSSION AND CONCLUSION: This study contributes to research on fairness assessment by focusing on the examination of systematic disparities and underscores the potential for revealing racial bias in machine learning models used in clinical settings.

Daily-Level Associations between Situational Familiarity with Location and People and Use of Alcohol-related Protective Behavioral Strategy Among Adolescents and Young Adults.

OBJECTIVE: Despite protective behavioral strategies (PBS) being an important part of alcohol prevention programs, utilization of PBS is sub-optimal, and research is needed to determine factors associated with use and non-use of PBS. The present study examined daily-level associations between situational familiarity (i.e., familiarity with locations and people) and the use of alcohol-related PBS among adolescents and young adults. METHOD: Participants (analysis N = 564, 55.1% females, 45.2% White, Non-Hispanic, ages 15 to 25, mean = 21.07 years [SD = 2.79]) were part of a longitudinal ecological momentary assessment burst study on cognitions and alcohol use. Mixed effects Poisson models were used to analyze data for engagement in PBS (i.e., serious harm reduction, stopping/limiting, and manner of drinking PBS). RESULTS: Within-person results indicated when participants had elevated (i.e., higher than their own average) familiarity with their location, they were less likely to use serious harm reduction PBS (Rate ratio [RR] = 0.94, p < 0.001) and stopping/limiting PBS (RR = 0.95, p < 0.001). Results showed that on drinking days with elevated familiarity with people, individuals were more likely to use serious harm reduction PBS (RR = 1.03, p = 0.01). There were no significant daily-level associations between familiarity with people or location and manner of drinking PBS. CONCLUSION: The study suggests PBS use, particularly for serious harm reduction and stopping/limiting strategies, varies among adolescents and young adults based on familiarity with location and people. Alcohol prevention interventions, including just-in-time interventions, should consider how to promote PBS use particularly in familiar locations and with less familiar people.

Exploring potential neuroimaging biomarkers for the response to non-steroidal anti-inflammatory drugs in episodic migraine.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are considered first-line medications for acute migraine attacks. However, the response exhibits considerable variability among individuals. Thus, this study aimed to explore a machine learning model based on the percentage of amplitude oscillations (PerAF) and gray matter volume (GMV) to predict the response to NSAIDs in migraine treatment. METHODS: Propensity score matching was adopted to match patients having migraine with response and nonresponse to NSAIDs, ensuring consistency in clinical characteristics and migraine-related features. Multimodal magnetic resonance imaging was employed to extract PerAF and GMV, followed by feature selection using the least absolute shrinkage and selection operator regression and recursive feature elimination algorithms. Multiple predictive models were constructed and the final model with the smallest predictive residuals was chosen. The model performance was evaluated using the area under the receiver operating characteristic (ROCAUC) curve, area under the precision-recall curve (PRAUC), balance accuracy (BACC), sensitivity, F1 score, positive predictive value (PPV), and negative predictive value (NPV). External validation was performed using a public database. Then, correlation analysis was performed between the neuroimaging predictors and clinical features in migraine. RESULTS: One hundred eighteen patients with migraine (59 responders and 59 non-responders) were enrolled. Six features (PerAF of left insula and left transverse temporal gyrus; and GMV of right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, and left precuneus) were observed. The random forest model with the lowest predictive residuals was selected and model metrics (ROCAUC, PRAUC, BACC, sensitivity, F1 score, PPV, and NPV) in the training and testing groups were 0.982, 0.983, 0.927, 0.976, 0.930, 0.889, and 0.973; and 0.711, 0.648, 0.639, 0.667,0.649, 0.632, and 0.647, respectively. The model metrics of external validation were 0.631, 0.651, 0.611, 0.808, 0.656, 0.553, and 0.706. Additionally, a significant positive correlation was found between the GMV of the left precuneus and attack time in non-responders. CONCLUSIONS: Our findings suggest the potential of multimodal neuroimaging features in predicting the efficacy of NSAIDs in migraine treatment and provide novel insights into the neural mechanisms underlying migraine and its optimized treatment strategy.

Remarkable Second Harmonic Generation Response in (C5H6NO)+(CH3SO3)-: Unraveling the Role of Hydrogen Bond in Thermal Driven Nonlinear Optical Switch.

Heat-activated second harmonic generation (SHG) switching materials are gaining interest for their ability to switch between SHG on and off states, offering potential in optoelectronic applications. The novel nonlinear optical (NLO) switch, (C5H6NO)+(CH3SO3)- (4-hydroxypyridinium methylsulfonate, 4HPMS), is a near-room-temperature thermal driven material with a strong SHG response (3.3 × KDP), making it one of the most potent heat-stimulated NLO switches. It offers excellent contrast of 13 and a high laser-induced damage threshold (2.5 × KDP), with reversibility > 5 cycles. At 73 °C, 4HPMS transitions from the noncentrosymmetric Pna21 room temperature phase (RTP) to the centrosymmetric P21/c phase, caused by the rotation of the (C5H6NO)+ and (CH3SO3)- due to partially thermal breaking of intermolecular hydrogen bonds. The reverse phase change exhibits a large 50 °C thermal hysteresis. Density functional theory (DFT) calculations show that (C5H6NO)+ primarily dictates both the SHG coefficient (dij) and birefringence (▵n(Zeiss) = 0.216 vs ▵n(cal.) = 0.202 at 546 nm; Δn(Immersion) = 0.210 vs ▵n(cal.) = 0.198 at 589.3 nm), while the band gap (Eg) is influenced synergistically by (C5H6NO)+ and (CH3SO3)-. Additionally, 4HPMS-RTP also exhibits mechanochromism upon grinding as well as an aggregation-enhanced emission in a mixture of acetone and water.

Oxidative stress induces release of mitochondrial DNA into the extracellular space in human placental villous trophoblast BeWo cells.

Circulating cell-free mitochondrial DNA (ccf-mtDNA) is an indicator of cell death, inflammation, and oxidative stress. ccf-mtDNA in pregnancies with placental dysfunction differs from that in healthy pregnancies, and the direction of this difference depends on gestational age and method of mtDNA quantification. Reactive oxygen species (ROS) trigger release of mtDNA, yet it is unknown whether trophoblast cells release mtDNA in response to oxidative stress, a common feature of pregnancies with placental pathology. We hypothesized that oxidative stress would induce cell death and release of mtDNA from trophoblast cells. BeWo cells were treated with antimycin A (10-320 µM) or rotenone (0.2-50 µM) to induce oxidative stress. A multiplex real-time quantitative PCR (qPCR) assay was used to quantify mtDNA and nuclear DNA in membrane-bound, non-membrane-bound, and vesicle-bound forms in cell culture supernatants and cell lysates. Treatment with antimycin A increased ROS (P < 0.0001), induced cell necrosis (P = 0.0004) but not apoptosis (P = 0.6471), and was positively associated with release of membrane-bound and non-membrane-bound mtDNA (P < 0.0001). Antimycin A increased mtDNA content in exosome-like extracellular vesicles (vesicle-bound form; P = 0.0019) and reduced autophagy marker expression (LC3A/B, P = 0.0002; p62, P < 0.001). Rotenone treatment did not influence mtDNA release or cell death (P > 0.05). Oxidative stress induces release of mtDNA into the extracellular space and causes nonapoptotic cell death and a reduction in autophagy markers in BeWo cells, an established in vitro model of human trophoblast cells. Intersection between autophagy and necrosis may mediate the release of mtDNA from the placenta in pregnancies exposed to oxidative stress.NEW & NOTEWORTHY This is the first study to test whether trophoblast cells release mitochondrial (mt)DNA in response to oxidative stress and to identify mechanisms of release and biological forms of mtDNA from this cellular type. This research identifies potential cellular mechanisms that can be used in future investigations to establish the source and biomarker potential of circulating mtDNA in preclinical experimental models and humans.

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in 18F-FDG PET Images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.