Accessing the spectrum of a single-photon by the Hong-Ou-Mandel interference.

We present and experimentally demonstrate a method for determining the spectral characterization of a single-photon state. This technique is based on the Hong-Ou-Mandel interference between a well-defined weak coherent state and a measured single-photon state. We estimate the spectrum of the single-photon state by fitting the measured interference dip with proposed model and least square method. Our method is particularly useful for characterising spectral property the single-photon state. It opens a way for robust and efficient on-line monitoring the single-photon emitters.

Predictors of futile recanalization after endovascular treatment of acute ischemic stroke.

OBJECTIVE: Endovascular therapy (EVT) is the most successful treatment for patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) in the anterior circulation. However, futile recanalization (FR) seriously affects the prognosis of these patients. The aim of this study was to investigate predictors of FR after EVT in patients with AIS. METHOD: Patients diagnosed with AIS due to anterior circulation LVO and receiving EVT between June 2020 and October 2022 were prospectively enrolled. FR after EVT was defined as a poor 90-day prognosis (modified Rankin Scale [mRS] score ≥ 3) despite achieving successful reperfusion (modified Thrombolysis in Cerebral Infarction [mTICI] classification of 2b-3). All included patients were categorized into control group (mRS score < 3) and FR group (mRS score ≥ 3). Demographic characteristics, comorbidities (hypertension, diabetes, atrial fibrillation, smoking, etc.), stroke-specific data (NIHSS score, ASPECT score and site of occlusion), procedure data (treatment type [direct thrombectomy vs. bridging thrombectomy], degree of vascular recanalization [mTICI], procedure duration time and onset-recanalization time), laboratory indicators (lymphocytes count, neutrophils count, monocytes count, C-reactive protein, neutrophil-to-lymphocyte ratio [NLR], monocyte-to-high-density lipoprotein ratio [MHR], lymphocyte-to-monocyte ratio [LMR], lymphocyte-to-C-reactive protein ratio [LCR], lymphocyte-to-high-density lipoprotein ratio[LHR], total cholesterol and triglycerides.) were compared between the two groups. Multivariate logistic regression analysis was performed to explore independent predictors of FR after EVT. RESULTS: A total of 196 patients were included in this study, among which 57 patients were included in the control group and 139 patients were included in the FR group. Age, proportion of patients with hypertension and diabetes mellitus, median NIHSS score, CRP level, procedure duration time, neutrophil count and NLR were higher in the FR group than in the control group. Lymphocyte count, LMR, and LCR were lower in the FR group than in the control group. There were no significant differences in platelet count, monocytes count, total cholesterol, triglycerides, HDL, LDL, gender, smoking, atrial fibrillation, percentage of occluded sites, onset-recanalization time, ASPECT score and type of treatment between the two groups. Multivariate logistic regression analysis demonstrated that NLR was independently associated with FR after EVT (OR = 1.37, 95%CI = 1.005-1.86, P = 0.046). CONCLUSION: This study demonstrated that high NLR was associated with a risk of FR in patients with AIS due to anterior circulation LVO. These findings may help clinicians determine which patients with AIS are at higher risk of FR after EVT. Our study can provide a theoretical basis for interventions in the aforementioned population.

Enhancing post-traumatic stress disorder patient assessment: leveraging natural language processing for research of domain criteria identification using electronic medical records.

BACKGROUND: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities. METHODS: In our study, we created a natural language processing (NLP) workflow to analyze electronic medical record (EMR) data and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, all-mpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories. RESULTS: The sentence transformer model demonstrated high F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. We found that 60.6% of PTSD women have at least one abnormal instance of the six RDoC domains as compared to PTSD men (51.3%), with 45.1% of PTSD women with higher levels of sensorimotor disturbances compared to men (41.3%). We also found that 57.3% of PTSD patients have at least one abnormal instance of the six RDoC domains based on our records. Also, veterans had the higher abnormalities of negative and positive valence systems (60% and 51.9% of veterans respectively) compared to non-veterans (59.1% and 49.2% respectively). The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption. CONCLUSIONS: The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

[Effects of Luhong Yixin Granules on myocardial fibrosis in heart failure rats based on TGF-ß1/Smads signaling pathway].

To investigate the effects of Luhong Yixin Granules on myocardial fibrosis in rats with heart failure and its possible mechanism, a total of 60 male Wistar rats were randomly divided into the control group, model group, and low-, medium-and high-dose Luhong Yixin Granules groups, with 12 rats in each group. Except for those in the control group, rats in the other groups were induced by intraperitoneal injection of doxorubicin(DOX) into a rat model. After the Luhong Yixin Granules were dissolved in the same amount of normal saline, they were given by gavage at low, medium and high doses(2.8, 5.6, 11.2 g·kg~(-1)·d~(-1)), and the control group and the model group were given the same amount of normal saline by gavage for 40 days. After the end of dosing, echocardiography was used to measure left ventricular ejection fraction(LVEF) and left ventricular fractional shortening(LVFS). Rat body weight(BW) and heart weight(HW) were calculated as HW/BW. Enzyme-linked immunosorbent assay was used to measure the levels of interleukin-6(IL-6), interleukin-17(IL-17), tumor necrosis factor-α(TNF-α), transforming growth factor-ß1(TGF-ß1), growth stimulation expressed gene 2 protein(ST2), N-terminal pro-B-type natriuretic peptide(NT-proBNP), galectin-3(Gal-3) and creatine kinase isoenzyme(CK-MB) in serum. Hematoxylin-eosin(HE) staining and Masson staining were used to observe the pathological morphology of myocardial tissue. Western blot and quantitative real-time polymerase chain reaction were used to detect the protein and mRNA expression levels of IL-6, IL-17, TNF-α, TGF-ß1, Smad3, Smad7, α-smooth muscle actin(α-SMA), and collagen Ⅰ(COL-Ⅰ), respectively. RESULTS:: showed that compared with those in the control group, LVEF, LVFS, and HW/BW in the model group were decreased(P<0.05), and the levels of IL-6, IL-17, TNF-α, TGF-ß1, ST2, NT-proBNP, Gal-3, and CK-MB were increased(P<0.05). HE staining showed inflammatory changes in myocardial tissue; Masson staining showed decreases in the cross-sectional area and ventricular cavity area of the heart, and myocardial fibrosis of varying degrees(P<0.05). The protein and mRNA expression of IL-6, IL-17, TNF-α, TGF-ß1, Smad3, α-SMA, and COL-Ⅰ were increased(P<0.05), and the protein and mRNA expression of Smad7 protein was decreased(P<0.01). Compared with those in the model group, LVEF, LVFS and HW/BW of the low-, medium-and high-dose Luhong Yixin Granules groups were increased(P<0.05), and the levels of IL-6, IL-17, TNF-α, TGF-ß1, ST2, NT-proBNP, Gal-3 and CK-MB were decreased(P<0.05). HE staining showed gradually reduced inflammatory changes of myocardial tissue, and Masson staining showed increased cross-sectional area and ventricular cavity area of the heart and decreased area of myocardial fibrosis(P<0.05). The protein and mRNA expression levels of IL-6, IL-17, TNF-α, TGF-ß1, Smad3, α-SMA, and COL-Ⅰ were decreased(P<0.05), while the protein and mRNA expression levels of Smad7 were increased(P<0.05). Luhong Yixin Granules may be of great value in the treatment of heart failure by regulating the TGF-ß1/Smads signaling pathway, inhibiting the expression of inflammation-related proteins, reducing the deposition of extracellular matrix, and alleviating myocardial fibrosis.

Cobalt-based Co3Mo3N/Co4N/Co Metallic Heterostructure as a Highly Active Electrocatalyst for Alkaline Overall Water Splitting.

Alkaline water electrolysis holds promise for large-scale hydrogen production, yet it encounters challenges like high voltage and limited stability at higher current densities, primarily due to inefficient electron transport kinetics. Herein, a novel cobalt-based metallic heterostructure (Co3Mo3N/Co4N/Co) is designed for excellent water electrolysis. In operando Raman experiments reveal that the formation of the Co3Mo3N/Co4N heterointerface boosts the free water adsorption and dissociation, increasing the available protons for subsequent hydrogen production. Furthermore, the altered electronic structure of the Co3Mo3N/Co4N heterointerface optimizes ΔGH of the nitrogen atoms at the interface. This synergistic effect between interfacial nitrogen atoms and metal phase cobalt creates highly efficient active sites for the hydrogen evolution reaction (HER), thereby enhancing the overall HER performance. Additionally, the heterostructure exhibits a rapid OH- adsorption rate, coupled with great adsorption strength, leading to improved oxygen evolution reaction (OER) performance. Crucially, the metallic heterojunction accelerates electron transport, expediting the afore-mentioned reaction steps and enhancing water splitting efficiency. The Co3Mo3N/Co4N/Co electrocatalyst in the water electrolyzer delivers excellent performance, with a low 1.58 V cell voltage at 10 mA cm-2, and maintains 100 % retention over 100 hours at 200 mA cm-2, surpassing the Pt/C||RuO2 electrolyzer.

[Application of metagenomic next-generation sequencing of bronchoalveolar lavage fluid in the diagnosis and treatment of refractory pneumonia in children]. / æ”¯æ°”ç®¡è‚ºæ³¡çŒæ´—æ¶²å®åŸºå› ç»„äºŒä»£æµ‹åºåœ¨å„¿ç«¥éš¾æ²»æ€§è‚ºç‚Žè¯Šæ²»ä¸­çš„åº”ç”¨.

OBJECTIVES: To investigate the clinical application of metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF) in the etiological diagnosis and treatment of refractory pneumonia (RTP) in children. METHODS: A retrospective analysis was performed on 160 children with RTP who were admitted to the Department of Pediatric Internal Medicine, Maternal and Child Health Hospital of Inner Mongolia Autonomous Region, from January 2020 to March 2023. According to whether mNGS was performed, they were divided into two groups: mNGS (n=80) and traditional testing (n=80). All children received the tests of inflammatory markers and pathogen tests after admission. Traditional pathogenicity tests included microbial culture (sputum specimen collected by suction tube), nucleic acid detection of respiratory pathogens, and serological test (mycoplasma, tuberculosis, and fungi). For the mNGS group, BALF specimens were collected after bronchoscopy and were sent to the laboratory for mNGS and microbial culture. The two groups were analyzed and compared in terms of the detection of pathogens and treatment. RESULTS: Compared with the traditional testing group, the mNGS group had a significantly higher detection rate of pathogens (92% vs 58%, P<0.05), with more types of pathogens and a higher diagnostic rate of mixed infections. Compared with the traditional testing group, the mNGS group had a significantly higher treatment response rate and a significantly lower incidence rate of complications during hospitalization (P<0.05). Treatment was adjusted for 68 children in the mNGS group according to the results of mNGS, with a treatment response rate of 96% (65/68) after adjustment. CONCLUSIONS: Compared with traditional pathogen tests, BALF mNGS can significantly improve the detection rate of pathogens and find some rare pathogens. In clinical practice, when encountering bottlenecks during the diagnosis and treatment of children with RTP, it is advisable to promptly perform the mNGS to identify the pathogens.

Dynamic risk factors of psychiatric readmission for major depressive disorder: A longitudinal study on patients treated with mono-antidepressant.

In this comprehensive study, we sought to unravel the risk factors for recurrence in Major Depressive Disorder (MDD), uniquely focusing on patients undergoing mono-antidepressant treatment. By considering psychiatric readmission as a direct indicator of MDD recurrence, we meticulously analyzed the records of 1,456 inpatients from a Chinese mental health center from 2012 to 2020. Our follow-up periods, spanning 90, 180, and 365 days post-discharge, allowed for a nuanced understanding of the recurrence dynamics. We identified four critical risk factors: thyroid function (FT3 and TSH), high-density lipoprotein (HDL) levels, and region of residence. Notably, the study revealed an increasing risk of readmission associated with decreased FT3 and HDL over time, while elevated TSH and residing in another province 's impact diminished. The antidepressant type did not significantly alter readmission risks, providing a unique perspective on MDD management. This research contributes to the field by offering a deeper understanding of how demographic and biochemical factors influence the likelihood of MDD recurrence, guiding more effective treatment approaches.

Trajectories and comorbid symptom networks of posttraumatic stress symptoms in frontline rescuers: A longitudinal study.

BACKGROUND: Previous research has largely lacked studies that explore the trajectories of Posttraumatic stress symptoms (PTSS) and the structure of comorbid psychiatric symptom networks following traumatic event, while controlling for the severity of traumatic exposure. The present study aims to explore the characteristic trajectories of PTSS, in the context of ensuring controlled levels of traumatic exposure. Furthermore, the PTSS, depressive, and anxiety comorbid symptom networks of different PTSS trajectory subgroups are also investigated. METHODS: A total of 296 frontline rescue personnel were enrolled into our study. In an effort to control for variations in traumatic exposure severity, this study ensured that all participants had same responsibilities and cumulative operational duration at the post-disaster rescue circumstance. Growth mixture models (GMMs) were employed to scrutinize the trajectories of PTSS. Additionally, network analysis was used to examine the comorbid symptom network of PTSS, depression, and anxiety. RESULTS: Four distinct PTSS trajectories were identified, namely Persisting Symptom, Gradual Recovery, Gradual Aggravation, and Asymptomatic. Although both the Persisting Symptom and Gradual Aggravation groups belong to the high-risk subgroups for persistent PTSS, they exhibit differences in core symptoms within their respective networks. The core symptom for the Persisting Symptom Network is flashbacks, while for the Gradual Aggravation Network, it is sleep disturbances. CONCLUSION: To the best of our knowledge, the present study represents the first research endeavor to integrate longitudinal trajectory analysis of PTSS with longitudinal symptom network analysis, clarifying the evolving features of PTSS but also offering valuable insights for early screening and intervention strategies.

Hydrogel-Functionalized Bandages with Janus Wettability for Efficient Unidirectional Drug Delivery and Wound Care.

Chronic wounds have imposed a severe physical and economic burden on the global healthcare system, which are usually treated by the delivery of drugs or bioactive molecules to the wound bed through wound dressings. In this work, we have demonstrated a hydrogel-functionalized bandage with Janus wettability in a bilayer structure to achieve unidirectional drug delivery and multifunctional wound care. The Janus patterned bandage with porous gradient wetting channels on the upper layer is responsible for the unidirectional transport of the drug from the outside to the wound bed (up to 90% drug transport efficiency) while preventing drug diffusion in unwanted directions (<8%). The hydrogel composed of chitosan quaternary ammonium salt (HACC), poly(vinyl alcohol) (PVA), and poly(acrylic acid) (PAA) at the bottom layer further functionalized such a bandage with biocompatibility, excellent antibacterial properties, and hemostatic ability to promote wound healing. Especially, the hydrogel-functionalized bandage with Janus wettability exhibits excellent mechanical flexibility (∼198% strain), which can comply well with skin deformation (stretching, bending, or twisting) and maintain unidirectional drug delivery behavior without any leakage. The in vivo full-thickness skin wound model confirms that the hydrogel-functionalized bandage can significantly facilitate epithelialization and collagen deposition and improve drug delivery efficiency, thus promoting wound closure and healing (the wound healing ratio was 98.10% at day 15). Such a synergistic strategy of unidirectional drug delivery and multifunctional wound care provides a more efficient, economical, and direct method to promote wound healing, which could be used as a potential high-performance wound dressing for clinical application.

Exploring the differential effects of psychological resilience and social support in mitigating post-traumatic psychiatric symptoms: real-world network analysis of front-line rescuers.

BACKGROUND: Although both psychological resilience and social support are widely believed to be effective in alleviating post-traumatic psychiatric symptoms in individuals with traumatic events, there has been a lack of comparative analysis of their intervention effects on different post-traumatic psychiatric symptoms. Furthermore, previous studies have mostly failed to control for potential confounding effects caused by different traumatic events. AIMS: We used the novel network analysis approach to examine the differential moderating effects of psychological resilience and social support on post-traumatic psychiatric symptoms, controlling for the confounding effects of traumatic events. METHOD: We recruited 264 front-line rescuers who experienced the same traumatic event. Quantified edge weights and bridge expected influence (BEI) were applied to compare the alleviating effects of psychological resilience and social support. RESULTS: Our study revealed distinct correlations in a sample of front-line rescuers: social support negatively correlates more with psychosomatic symptoms, notably fatigue in depressive networks and sleep disturbance in post-traumatic stress disorder (PTSD) networks, whereas psychological resilience shows fewer such correlations. Quantitative analysis using BEI indicated that psychological resilience more effectively suppresses depressive and anxiety symptom networks, whereas social support more significantly inhibits PTSD symptom networks. CONCLUSIONS: The current study represents the first attempt to examine the differential effects of psychological resilience and social support on post-traumatic outcomes in real-world emergency rescuers, controlling for the confounding effect of traumatic events. Our results can act as the theoretical reference for future precise and efficient post-trauma psychological interventions.

Monolayer Cu2Se: a topological catalysis in CO2electroreduction.

This investigation provides a comprehensive exploration into the intricate interplay between topological surface states (TSS) and catalytic performance in two-dimensional (2D) materials, with specific emphasis on monolayer Cu2Se. Leveraging the unique characteristics of nodal loop semimetals (NLSMs), we delve into the precise influence of TSS on catalytic activity, particularly in the domain of CO2electrochemical reduction. Our findings illuminate the central role played by these TSS, arising from the underlying NLSM framework, in sculpting catalytic efficiency. The length of these surface states emerges as a critical determinant of surface density of states (DOSs), a fundamental factor governing catalytic behavior. Extension of these surface states correlates with heightened surface DOSs, yielding lower Gibbs free energies and consequently enhancing catalytic performance, particularly in the electrochemical reduction of CO2. Moreover, we underscore the profound importance of preserving symmetries that protect the nodal loop. The disruption of these symmetries is found to result in a significant degradation of catalytic efficacy, underscoring the paramount significance of topological features in facilitating catalytic processes. Therefore, this study not only elucidates the fundamental role of TSS in dictating the catalytic performance of topological 2D materials but also paves the way for harnessing these unique attributes to drive sustainable and highly efficient catalysis across a diverse spectrum of chemical processes.

Blood Pressure Estimation Based on PPG and ECG Signals Using Knowledge Distillation.

OBJECTIVE: Easy access bio-signals are useful for alleviating the shortcomings and difficulties associated with cuff-based and invasive blood pressure (BP) measurement techniques. This study proposes a deep learning model, trained using knowledge distillation, based on photoplethysmographic (PPG) and electrocardiogram (ECG) signals to estimate systolic and diastolic blood pressures. METHODS: The estimation model comprises convolutional layers followed by one bidirectional recurrent layer and attention layers. The training approach involves knowledge distillation, where a smaller model (student model) is trained by leveraging information from a larger model (teacher model). RESULTS: The proposed multistage model was evaluated on 1205 subjects from Medical Information Mart for Intensive Care (MIMIC) III database using the Association for the Advancement of Medical Instrumentation (AAMI) and the standards of the British Hypertension Society (BHS). The results revealed that our model performance achieved grade A in estimating both systolic blood pressure (SBP) and diastolic blood pressure (DBP) and met the requirements of the AAMI standard. After training with knowledge distillation (KD), the model achieved a mean absolute error and standard deviation of 2.94 ± 5.61 mmHg for SBP and 2.02 ± 3.60 mmHg for DBP. CONCLUSION: Our results demonstrate the benefits of the knowledge distillation training method in reducing the number of parameters and improving the predictive accuracy of the blood pressure regression model.

[Ecological network optimization of Jiuquan City, Gansu, China based on complex network theory and circuit model]. / åŸºäºŽå¤æ‚ç½‘ç»œç†è®ºå’Œç”µè·¯æ¨¡åž‹çš„é ’æ³‰å¸‚ç”Ÿæ€ç½‘ç»œä¼˜åŒ–.

Building a scientific and reasonable ecological network is the key for optimizing the pattern of territorial development and protection, and is of great significance for ensuring regional ecological security and promoting the virtuous cycle of ecosystems. In previous studies, nodal attack method (destruction of ecological source area) was often used in the "robustness" evaluation of ecological networks. Actually, the ecological corridor is more fragile than the source area, and thus the nodal attack method is not reasonable. In this study, taking Jiuquan City as the research area, based on the circuit model to construct the ecological network, we carried out the topology optimization of ecological network by using three strategies (random edge increase, node degree and priority edge increase with low node intermedium number) in complex network theory. We compared and analyzed the "robustness" of ecological network before and after optimization by constructing edge attack strategy, and selected the best network optimization strategy. The results showed that 65 ecological source areas were identified in Jiuquan City, with a total area of 20275.15 km2, and that grassland accounted for 89.5% of the source area. We identified 179 ecological corridors with a total length of 6387.16 km, 158 ecological barrier points with a total area of 1385.5 km2. The unused land accounted for 92.2% of the total barrier points area. We identified 63 ecological pinch points, mainly concentrated in the source edge and corridor intersection. Among them, the spatial distribution of 11 barrier points and pinch points was consistent, which was the key area to be repaired in ecological network optimization. The three optimization strategies had significantly improved the stability of ecological network in Jiuquan City. The relative size of the maximum connected subgraph and the edge connected rate of the ecological network of the optimization strategy of adding edges according to degree were all the most stable under random attack mode and deliberate attack mode, which was the best optimization scheme for ecological network in Jiuquan City.

Predicting central cervical lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis: a practical nomogram based on retrospective study.

Background: In papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent. Methods: Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT. Then a prediction model was developed and visualized using a nomogram. The stability of the model was assessed using ten-fold cross-validation. The performance of the model was further evaluated through the use of ROC curve, calibration curve, and decision curve analysis. Results: A total of 376 HT PTC patients were included in this study, comprising 162 patients with CLNM and 214 patients without CLNM. The results of the multivariate logistic regression analysis revealed that age, Tg-Ab level, tumor size, punctate echogenic foci, and blood flow grade were identified as independent risk factors associated with the development of CLNM in HT PTC. The area under the curve (AUC) of this model was 0.76 (95% CI [0.71-0.80]). The sensitivity, specificity, accuracy, and positive predictive value of the model were determined to be 88%, 51%, 67%, and 57%, respectively. Conclusions: The proposed clinic-ultrasound-based nomogram in this study demonstrated a favorable performance in predicting CLNM in HT PTCs. This predictive tool has the potential to assist clinicians in making well-informed decisions regarding the appropriate extent of surgical intervention for patients.

[Spatio-temporal Differentiation of County Carbon Budget and Territorial Space Optimization Zoning Strategy in Gansu Province from the Perspective of Carbon Neutrality].

Under the background of comprehensively practicing the overall system concept of the "living community" in the new era, incorporating the carbon neutral development goal into the territorial spatial planning and construction and establishing the territorial spatial pattern and optimization strategy in line with the actual development of Gansu Province are of great significance for promoting the comprehensive green low-carbon transformation and high-quality development of regional economy and society. Taking counties in Gansu Province as an example, based on the perspective of carbon neutrality research, the land use carbon budget of 87 counties in Gansu Province in 2010, 2015, and 2021 was calculated and analyzed. GIS spatial analysis and social network analysis were used to further explore their spatial differentiation characteristics and the overall characteristics of the carbon emission spatial correlation network. At last, combined with the main function zoning, the low-carbon oriented land space optimization zoning was carried out, and differentiated low-carbon development strategies were proposed. The results were as follows： ① Carbon emissions in Gansu Province showed an upward trend, but the increase rate decreased, showing a spatial distribution of "high in the central and eastern part of the country, low in the southwest." Construction land was the main carbon source. The carbon uptake showed a spatial distribution of "high in the south and low in the north, high in the west and low in the east." Woodlands were the main carbon sinks. The net carbon emissions showed an increasing trend, and approximately 58.62% of the counties in the province were in a carbon imbalance situation. ② In 2021, the spatial network of county carbon emissions was closely related, showing a "core-edge" pattern. The Chenguan District and Qilihe District were in the core position of the network and received more correlation relationships in the network. The network contacts in Longzhong area were frequent, followed by the contacts in Longdongnan area. ③ Based on carbon emissions, carbon sequestration, and ecological carrying capacity coefficients and using the results of spatial correlation of social networks as role positions, the province was divided into four carbon-neutral sub-districts. At the same time, superimposed analysis of the main function zoning, the county area of the province was reconstructed into seven territorial space zones, and the differentiated regional low-carbon optimization development strategy was proposed for each zone.

An Effective Ultrasound Features-Based Diagnostic Model via Principal Component Analysis Facilitated Differentiating Subtypes of Mucinous Breast Cancer From Fibroadenomas.

BACKGROUND: Mucinous breast carcinoma (MBC) is often misdiagnosed as fibroadenoma (FA),which can lead to inappropriate or delayed treatments. This study aimed to establish an efficient ultrasound (US)-based diagnostic model to distinguish MBC subtypes from FAs. METHODS: Between January 2017 and February 2024, 240 lesions were enrolled, comprising 65 cases of pure mucinous breast carcinoma (PMBC), 47 cases of mixed mucinous breast carcinoma (MMBC), and 128 cases of FAs. Ten US feature variables underwent principal component analysis (PCA). Models were constructed based on components explaining over 75% of the total variation, with varimax rotation applied for interpretability. Comprehensive models were developed to distinguish PMBCs and MMBCs from FAs. RESULTS: Six principal components were selected, achieving a cumulative contribution rate of 77.46% for PMBCs vs. FAs and 78.62% for MMBCs vs. FAs. The principal component of cystic-solid composition and posterior acoustic enhancement demonstrated the highest diagnostic value for distinguishing PMBCs from FAs (AUC: 0.86, ACC: 80.31%). Features including vascularization, irregular shape, ill-defined border, and larger size exhibited the highest diagnostic value for distinguishing MMBCs from FAs (AUC: 0.90, ACC: 87.43%). The comprehensive models showed excellent clinical value in distinguishing PMBCs (AUC = 0.86, SEN = 86.15%, SPE = 73.44%, ACC = 77.72%) and MMBCs (AUC = 0.92, SEN = 80.85%, SPE = 95.31%, ACC = 91.43%) from FAs. CONCLUSION: This diagnostic model holds promise for effectively distinguishing PMBCs and MMBCs from FAs, assisting radiologists in mitigating diagnostic biases and enhancing diagnostic efficiency.

Enhancing post-traumatic stress disorder patient assessment: Leveraging Natural Language Processing for Research of Domain Criteria Identification using electronic medical records.

Background: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities. Methods: In our study, we created an NLP workflow to analyze electronic medical record (EMR) data, and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, allmpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories. Results: The sentence transformer model demonstrated superior F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. Women had the highest abnormalities of sensorimotor systems, while veterans had the highest abnormalities of negative and positive valence systems. The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption. Conclusions: The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

Predictive Role of Pre-Thrombolytic Neutrophil-Platelet Ratio on Hemorrhagic Transformation After Intravenous Thrombolysis in Acute Ischemic Stroke.

To investigate the predictive role of the neutrophil-platelet ratio (NPR) before intravenous thrombolysis (IVT) on hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS). AIS patients treated with IVT without endovascular therapy between June 2019 and February 2023 were included. Patients were divided into high NPR (>35) and low NPR (≤35) groups according to the optimal threshold NPR value for identifying high-risk patients before IVT. The baseline data and the incidence of HT and symptomatic intracranial hemorrhage (sICH) were compared between the two groups. The predictive role of the NPR and other related factors on HT after IVT was analyzed by multivariate logistic regression. A total of 247 patients were included, with an average age of 67.5 ± 12.4 years. Post-thrombolytic HT was observed in 18.6% of the patients, and post-thrombolytic sICH was observed in 1.2% of the patients. There were 69 patients in the high NPR group and 178 patients in the low NPR group. The incidence of HT in the high NPR group was significantly higher than that in the low NPR group (30.4% vs 16.3%, P < .05). The incidence of sICH was significantly higher in the high NPR group than in the low NPR group (14.5% vs 1.7%, P < .001). Multivariate logistic regression analysis showed that NPR > 35 was positively correlated with HT (odds ratio (OR) = 3.236, 95% confidence interval (CI): 1.481-7.068, P = .003) and sICH (OR = 13.644, 95% CI: 2.392-77.833, P = .003). A high NPR (>35) before IVT may be a predictor of HT in AIS patients. This finding may help clinicians make clinical decisions before IVT in AIS patients.

Prediction of adverse events risk in patients with comorbid post-traumatic stress disorder and alcohol use disorder using electronic medical records by deep learning models.

BACKGROUND: Identifying co-occurring mental disorders and elevated risk is vital for optimization of healthcare processes. In this study, we will use DeepBiomarker2, an updated version of our deep learning model to predict the adverse events among patients with comorbid post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD), a high-risk population. METHODS: We analyzed electronic medical records of 5565 patients from University of Pittsburgh Medical Center to predict adverse events (opioid use disorder, suicide related events, depression, and death) within 3 months at any encounter after the diagnosis of PTSD+AUD by using DeepBiomarker2. We integrated multimodal information including: lab tests, medications, co-morbidities, individual and neighborhood level social determinants of health (SDoH), psychotherapy and veteran data. RESULTS: DeepBiomarker2 achieved an area under the receiver operator curve (AUROC) of 0.94 on the prediction of adverse events among those PTSD+AUD patients. Medications such as vilazodone, dronabinol, tenofovir, suvorexant, modafinil, and lamivudine showed potential for risk reduction. SDoH parameters such as cognitive behavioral therapy and trauma focused psychotherapy lowered risk while active veteran status, income segregation, limited access to parks and greenery, low Gini index, limited English-speaking capacity, and younger patients increased risk. CONCLUSIONS: Our improved version of DeepBiomarker2 demonstrated its capability of predicting multiple adverse event risk with high accuracy and identifying potential risk and beneficial factors.

DeepBiomarker2: Prediction of Alcohol and Substance Use Disorder Risk in Post-Traumatic Stress Disorder Patients Using Electronic Medical Records and Multiple Social Determinants of Health.

Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. We developed DeepBiomarker2 by leveraging deep learning and natural language processing to analyze lab tests, medication use, diagnosis, social determinants of health (SDoH) parameters, and psychotherapy for outcome prediction. To increase the model's interpretability, we further refined our contribution analysis to identify key features by scaling with a factor from a reference feature. We applied DeepBiomarker2 to analyze the EMR data of 38,807 patients from the University of Pittsburgh Medical Center diagnosed with post-traumatic stress disorder (PTSD) to determine their risk of developing alcohol and substance use disorder (ASUD). DeepBiomarker2 predicted whether a PTSD patient would have a diagnosis of ASUD within the following 3 months with an average c-statistic (receiver operating characteristic AUC) of 0.93 and average F1 score, precision, and recall of 0.880, 0.895, and 0.866 in the test sets, respectively. Our study found that the medications clindamycin, enalapril, penicillin, valacyclovir, Xarelto/rivaroxaban, moxifloxacin, and atropine and the SDoH parameters access to psychotherapy, living in zip codes with a high normalized vegetative index, Gini index, and low-income segregation may have potential to reduce the risk of ASUDs in PTSD. In conclusion, the integration of SDoH information, coupled with the refined feature contribution analysis, empowers DeepBiomarker2 to accurately predict ASUD risk. Moreover, the model can further identify potential indicators of increased risk along with medications with beneficial effects.