Exploratory research on the effective chemical basis of Tanreqing injection for treating acute lung injury: in vivo, in vitro and in silico.

ETHNOPHARMACOLOGICAL RELEVANCE: Sepsis-induced acute lung injury (ALI) presents with significant morbidity and mortality in clinical settings. Tanreqing Injection (TRQI) has been clinically recommended for the treatment of ALI; however, the specific active chemical constituents remain unidentified. AIM OF THE STUDY: This study aimed to elucidate the potential pharmacologically active components and the underlying mechanisms of TRQI in the treatment of sepsis-induced ALI. MATERIALS AND METHODS: High-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) techniques were employed to identify the effective chemical constituents of TRQI. Additionally, an in vitro study was conducted using Raw264.7 macrophage cells stimulated with lipopolysaccharide (LPS) to evaluate the inhibitory effects of TRQI. An acute lung injury model produced by LPS was intraperitoneal injection in mice to assess the ALI-inhibitory effect of TRQI. The lung's pathological characteristics were examined using hematoxylin and eosin staining. Enzyme-linked immunosorbent assay (ELISA) and QPCR were performed to confirm the pharmaceutical effect. Network pharmacology was employed for mechanistic exploration, incorporating GO, and PPI analyses of targets. Src inhibitor and JNK agonist used to investigate the dependence of associated signaling pathways. RESULTS: Combining pharmacokinetic characteristics, lung first-pass effect and anti-inflammatory effects, the main components of TRQI for treating sepsis induced ALI were narrowed down to seven compounds: chlorogenic acid, scutellarin, wogonoside, oroxyloside, oroxylin A and baicalein. Network pharmacology indicated that Src/JNK signaling pathway, may be the main regulatory pathway for treatment of actue lung injury. Next by using Src inhibitor, Src inhibition partly diminished the protective effects of TRQI in LPS-injected mice. Pretreatment with JNK agonist anisomycin abolished the protective effects of lung injury in vivo. CONCLUSIONS: TRQI is injected, the seven compounds could be presented in vivo, which can improve ALI by inhibiting Src-JNK signaling.

Influence of ore-drawing port position on ore-rock flow characteristics in ore pass and lateral pressure on ore pass wall.

The blockage and the deformation and failure of the ore pass walls constitute two major problems in applying the ore passes in mines. These problems, which affect the normal operation of mine production, have attracted widespread attention worldwide. The labeled-particle layers method based on numerical simulation was used to investigate the flow characteristics of the ore-rock bulk in the ore pass under different eccentric distances of the ore-drawing port center and ore pass centerline. Moreover, the overpressure coefficient and overpressure number are used to evaluate the degree of damage to the ore-pass wall. The results show that: (1) During the ore drawing process under different eccentricities, the flow patterns of the topmost labeled-particle layers in the ore pass are always in a "-" shaped distribution, and the other layers in the ore pass gradually transition from a "-" shape to a "U" shaped distribution, and then gradually to a "V" shape closer to the drawing funnel; (2) in the range of the ore-drawing funnel, the flow pattern of the ore-rock bulk gradually changes from an upright "V" shape to an italic "V" shape with increasing eccentricity and tip slants to the drawing port, and is less affected in the shaft; and (3) the dynamic lateral pressure caused by the ore-rock flow mainly acts on the lower part of the storage section. When the eccentricity is 0.5 m, the maximum overpressure coefficient and overpressure times are the smallest, leading to the lowest damage degree of the ore pass wall.

Several factors predict the achievement of the patient acceptable symptom state and minimal clinically important difference for patient-reported outcome measures following anterior cruciate ligament reconstruction: A systematic review.

PURPOSE: To summarize the predictors of the patient acceptable symptom state (PASS), minimal clinically important difference (MCID) and minimal important change (MIC) for patient-reported outcome measures (PROMs) following anterior cruciate ligament reconstruction (ACLR). METHODS: MEDLINE, PubMed and Embase were searched from inception to 5 January 2024. The authors adhered to PRISMA/R-AMSTAR guidelines, and the Cochrane Handbook for Systematic Reviews of Interventions. Data on statistical associations between predictive factors and PROMs were extracted. Inverse odds ratios (ORs) and confidence intervals (reverse group comparison) were calculated when appropriate to ensure comparative consistency. RESULTS: Thirteen studies comprising 21,235 patients (48.1% female) were included (mean age 29.3 years). Eight studies comprising 3857 patients identified predictors of PASS, including lateral extra-articular tenodesis (LET) (OR = 11.08, p = 0.01), hamstring tendon (HT) autografts (OR range: 2.02-2.63, p ≤ 0.011), age over 30 (OR range: 1.37-2.28, p ≤ 0.02), male sex (OR range: 1.03-1.32, p ≤ 0.01) and higher pre-operative PROMs (OR range: 1.04-1.21). Eight studies comprising 18,069 patients identified negative predictors of MCID or MIC, including female sex (OR = 0.93, p = 0.034), absence of HT autografts (OR = 0.70, p < 0.0001), higher pre-operative PROMs (OR = 0.76-0.84, p ≤ 0.01), meniscectomy (OR = 0.67, p = 0.014) and collision sports (OR = 0.02-0.60, p ≤ 0.05). CONCLUSION: Higher pre-operative PROMs, age over 30, male sex, LETs and HT autografts predicted PASS achievement. Lower pre-operative PROMs, male sex, non-collision sports, and lack of meniscectomies predicted MCID/MIC achievement. This review provides a comprehensive understanding of the predictors of clinically significant post-ACLR outcomes, thus improving clinical decision-making and the management of patient expectations. LEVEL OF EVIDENCE: Level IV.

Regional variations and drivers of essential and non-essential elements in Steller sea lion pups from the Aleutian Islands, Alaska.

Exposure and resulting tissue concentrations of various elements from natural and anthropogenic sources are influenced by multiple factors, such as geographic location, age, diet, and metabolic rate, that can influence wildlife health. Essential and non-essential elements were assessed in lanugo and whole blood collected in 2019 from 102 Steller sea lion (Eumetopias jubatus) pups from two rookeries from the western and central Aleutian Islands: Agattu (WAI, n = 54) and Ulak Islands (CAI, n = 48). Rookery, sex, dorsal standard length, and trophic ecology (ẟ15N, ẟ13C values) effects on element concentration were evaluated. Significant differences in element concentrations of lanugo were exhibited across rookeries (p < 0.05), except for zinc (Zn). For example, higher mercury (Hg) and selenium (Se) concentrations were observed in WAI than CAI, while other elements were lower in WAI. Whole blood showed higher sulfur (S) and Se concentrations in CAI compared to WAI, while WAI had elevated strontium (Sr) and Hg concentrations relative to CAI. Trophic ecology significantly influenced most element concentrations, possibly due to regional variations in adult female feeding and food web dynamics. Interactions between elements were found in lanugo across both rookeries, with varying strengths. Whole blood displayed less pronounced yet consistent associations, with variable intensities. Essential elements sodium (Na), potassium (K), and calcium (Ca) formed a distinct group whose interaction is crucial for nervous system function and muscle contraction. Another group comprised zinc (Zn), iron (Fe), manganese (Mn), magnesium (Mg), phosphorous (P), S, and Se, which are known for indirectly interacting with enzyme function and metabolic pathways. Hg and Se formed a distinct group probably due to their known chemical interactions and physiological protective interactions.

Evaluation and integration of cell-free DNA signatures for detection of lung cancer.

Cell-free DNA (cfDNA) analysis has shown potential in detecting early-stage lung cancer based on non-genetic features. To distinguish patients with lung cancer from healthy individuals, peripheral blood were collected from 926 lung cancer patients and 611 healthy individuals followed by cfDNA extraction. Low-pass whole genome sequencing and targeted methylation sequencing were conducted and various features of cfDNA were evaluated. With our customized algorithm using the most optimal features, the ensemble stacked model was constructed, called ESim-seq (Early Screening tech with Integrated Model). In the independent validation cohort, the ESim-seq model achieved an area under the curve (AUC) of 0.948 (95% CI: 0.915-0.981), with a sensitivity of 79.3% (95% CI: 71.5-87.0%) across all stages at a specificity of 96.0% (95% CI: 90.6-100.0%). Specifically, the sensitivity of the ESim-seq model was 76.5% (95% CI: 67.3-85.8%) in stage I patients, 100% (95% CI: 100.0-100.0%) in stage II patients, 100% (95% CI: 100.0-100.0%) in stage III patients and 87.5% (95% CI: 64.6%-100.0%) in stage IV patients in the independent validation cohort. Besides, we constructed LCSC model (Lung Cancer Subtype multiple Classification), which was able to accurately distinguish patients with small cell lung cancer from those with non-small cell lung cancer, achieving an AUC of 0.961 (95% CI: 0.949-0.957). The present study has established a framework for assessing cfDNA features and demonstrated the benefits of integrating multiple features for early detection of lung cancer.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

BACKGROUND: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. METHODS: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). RESULTS: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). CONCLUSIONS: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

Background: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. Methods: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). Results: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). Conclusions: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

High Variability in Standardized Outcome Thresholds of Clinically Important Changes in Shoulder Instability Surgery: A Systematic Review.

PURPOSE: The purpose of this study was to examine reported MCID and PASS values for PROMs following shoulder instability surgery and assess variability in published values depending on the surgery performed. Secondarily, our aims were to describe the methods used to derive MCID and PASS values in the published literature, including anchor-based, distribution-based, or other approaches, and to assess the frequency of MCID and PASS usage in studies on shoulder instability surgery. METHODS: A systematic review of MCID and PASS values following Bankart, Latarjet, and Remplissage procedures was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The Embase, Pubmed, and Cochrane Central Register of Controlled Trials (CENTRAL) databases were queried from 1985 to 2023. Inclusion criteria included studies written in English, and studies reporting utilization MCID or PASS for patient reported outcome measures (PROMS) following Latarjet, Bankart, Remplissage approaches for shoulder instability surgery. Extracted data included study population characteristics, intervention characteristics, and outcomes of interest. Continuous data were described using median and range. Categorical variables, including PROMs reported and MCID/PASS methods, were described using percentages. As MCID is a patient-level metric and not a group-level metric, the authors validated that all included studies reported proportions (%) of subjects that met or exceeded the MCID. RESULTS: A total of 174 records were screened, and 8 studies were included in this review. MCID was the most widely utilized outcome threshold which was reported in all 8 studies, with only 2 studies reporting both the MCID and the PASS. The most widely studied PROMs were the American Shoulder and Elbow Surgeons (ASES) (range 5.65-9.6 for distribution MCID, 8.5 anchor MCID, 86 anchor PASS); Single Assessment Numeric Evaluation (SANE) (range 11.4-12.4 distribution MCID, 82.5-87.5 anchor PASS); visual analog scale (VAS) (range 1.1-1.7 distribution MCID, 1.5-2.5 PASS); Western Ontario Shoulder Instability Index (WOSI) (range 60.7-254.9 distribution MCID, 126.43 anchor MCID, 571-619.5 anchor PASS); and Rowe scores (range 5.6-8.4 distribution MCID, 9.7 anchor MCID). Notably, no studies reported on substantial clinical benefit (SCB) or maximal outcome improvement (MOI). CONCLUSION: Despite the wide array of available PROMs for assessing shoulder instability surgery outcomes, the availability of clinically significant outcome thresholds such as MCID and PASS remains relatively limited. While MCID has been the most frequently reported metric, there is considerable inter-study variability observed in their values. CLINICAL RELEVANCE: Knowing the outcome thresholds such as MCID and PASS of the PROMs frequently used to evaluate the results of glenohumeral stabilization surgery is fundamental, since they allow us to know what is a clinically significant improvement for the patient.

Studies on pharmacokinetic properties and intestinal absorption mechanism of sanguinarine chloride: in vivo and in situ.

Sanguinarine (SAN) is an alkaloid with multiple biological activities, mainly extracted from Sanguinaria canadensis or Macleaya cordata. The low bioavailability of SAN limits its utilization. At present, the nature and mechanism of SAN intestinal absorption are still unclear. The pharmacokinetics, single-pass intestinal perfusion test (SPIP), and equilibrium solubility test of SAN in rats were studied. The absorption of SAN at 20, 40, and 80 mg/L in different intestinal segments was investigated, and verapamil hydrochloride (P-gp inhibitor), celecoxib (MPR2 inhibitor), and ko143 (BCRP inhibitor) were further used to determine the effect of efflux transporter proteins on SAN absorption. The equilibrium solubility of SAN in three buffer solutions (pH 1.2, 4.5 and 6.8) was investigated. The oral pharmacokinetic results in rats showed that SAN was rapidly absorbed (Tmax=0.5 h), widely distributed (Vz/F = 134 L/kg), rapidly metabolized (CL = 30 L/h/kg), and had bimodal phenomena. SPIP experiments showed that P-gp protein could significantly affect the effective permeability coefficient (Peff) and apparent absorption rate constant (Ka) of SAN. Equilibrium solubility test results show that SAN has the best solubility at pH 4.5. In conclusion, SAN is a substrate of P-gp, and its transport modes include efflux protein transport, passive transport and active transport.

An explorative analysis on the optimal cryo-passes and freezing time of the ultrathin cryoprobe in endobronchial ultrasound-guided transbronchial mediastinal cryobiopsy.

EBUS-guided transbronchial mediastinal cryobiopsy (TBMC) has emerged as a promising biopsy tool for diagnosing hilar and mediastinal pathologies. However, several fundamental technical aspects of TBMC remain unexplored. This study aims to determine the optimal number of cryo-passes and freezing time of the ultrathin cryoprobe in EBUS-TBMC concerning specimen size and procedural diagnostic yield. We conducted a retrospective chart review of patients with mediastinal and hilar lesions who underwent EBUS-TBMC between January 2021 and April 2023 across three hospitals in Malaysia. A total of 129 EBUS-TBMC procedures were successfully completed, achieving an overall diagnostic yield of 88.4%. Conclusive TBMC procedures were associated with larger specimen sizes (7.0 vs. 5.0 mm, p < 0.01). Specimen size demonstrated a positive correlation with diagnostic yield (p < 0.01), plateauing at specimen size of 4.1-6.0 mm. A significant positive correlation was also observed between the number of cryo-passes and both specimen size (p < 0.01) and diagnostic yield (p < 0.05). Diagnostic yield plateaued after 2-3 cryo-passes. In contrast, longer freezing times trended towards smaller specimens and lower diagnostic yield, though not reaching statistical significance. The highest diagnostic yield was recorded at the 3.1-4.0 s freezing time. The safety profile of TBMC remains favourable, with one case (0.8%) of pneumothorax and nine cases (7%) of self-limiting bleeding. In our cohort, TBMC performance with 2-3 cryo-passes and a 3.1-4.0 s freezing time to achieve a total aggregate specimen size of 4.1-6.0 mm appeared optimal. Further prospective studies are needed to validate these findings.

In silico analysis of potential inhibitors for breast cancer targeting 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) catalyses.

Breast cancer (BC) is still one of the major issues in world health, especially for women, which necessitates innovative therapeutic strategies. In this study, we investigated the efficacy of retinoic acid derivatives as inhibitors of 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), which plays a crucial role in the biosynthesis and metabolism of oestrogen and thereby influences the progression of BC and, the main objective of this investigation is to identify the possible drug candidate against BC through computational drug design approach including PASS prediction, molecular docking, ADMET profiling, molecular dynamics simulations (MD) and density functional theory (DFT) calculations. The result has reported that total eight derivatives with high binding affinity and promising pharmacokinetic properties among 115 derivatives. In particular, ligands 04 and 07 exhibited a higher binding affinity with values of -9.9 kcal/mol and -9.1 kcal/mol, respectively, than the standard drug epirubicin hydrochloride, which had a binding affinity of -8.2 kcal/mol. The stability of the ligand-protein complexes was further confirmed by MD simulations over a 100-ns trajectory, which included assessments of hydrogen bonds, root mean square deviation (RMSD), root mean square Fluctuation (RMSF), dynamic cross-correlation matric (DCCM) and principal component analysis. The study emphasizes the need for experimental validation to confirm the therapeutic utility of these compounds. This study enhances the computational search for new BC drugs and establishes a solid foundation for subsequent experimental and clinical research.

Prediction of Welding Deformation Using the Thermal Elastic-Plastic Finite Element Method by Considering Welding Interpass Temperature.

In this study, we propose a method for predicting welding deformation caused by multi-pass welding using the thermal elastic-plastic finite element method (TEP-FEM) by considering the interpass temperature. This method increases the interpass temperature, which has not been considered in the existing TEP-FEM, from 200 °C to 1000 °C, and simultaneously performs thermal and mechanical analyses. In addition, this method can also evaluate temperature history and the time it takes to weld. By predicting the welding deformation using this method, angular distortion prediction was reduced from 16.75 mm to 10.9 mm compared to the case where the interpass temperature was cooled to room temperature. Additionally, the deformation error was significantly reduced from 6.14% to 2.92% compared to that of the strain as directed boundary method used in a previous study. Additionally, our research demonstrated that interpass temperatures above 800 °C can result in increased deformation errors. In conclusion, it is essential to select an appropriate temperature to minimize deformation error.

Modeling biases from low-pass genome sequencing to enable accurate population genetic inferences.

Low-pass genome sequencing is cost-effective and enables analysis of large cohorts. However, it introduces biases by reducing heterozygous genotypes and low-frequency alleles, impacting subsequent analyses such as demographic history inference. We developed a probabilistic model of low-pass biases from the Genome Analysis Toolkit (GATK) multi-sample calling pipeline, and we implemented it in the population genomic inference software dadi. We evaluated the model using simulated low-pass datasets and found that it alleviated low-pass biases in inferred demographic parameters. We further validated the model by downsampling 1000 Genomes Project data, demonstrating its effectiveness on real data. Our model is widely applicable and substantially improves model-based inferences from low-pass population genomic data.

Exploring the sugar-sweetened beverage tax (SSBT) pass-through rate in the Irish hospitality sector.

BACKGROUND: The World Health Organization (WHO) supports the use of Sugar-Sweetened Beverage Taxes (SSBTs) as a fiscal lever to help reduce sugar consumption and tackle obesity. Obesity is associated with a range of adverse health outcomes. In response to increasing levels of obesity in Ireland, an SSBT was introduced in 2018. Previous research in Ireland has noted that the pass-through rate of the SSBT in retail (off-site consumption) settings was poor. However, to date, no research has examined the SSBT pass-through rate in hospitality (on-site consumption) venues in Ireland. METHODS: This research examines the SSBT pass-through rate on Coca-Cola versus diet versions of Coca-Cola in a convenience sample of 100 hospitality venues in two provincial Irish cities. RESULTS: Wilcoxon signed rank test analysis revealed that regular Coca-Cola was significantly more expensive compared to the price charged for diet versions of Coca-Cola. However, in 85.6% of cases the same price was charged for both full-sugar and sugar-free drinks. The mean pass-through rate of the SSBT was 33.8%. CONCLUSION: The effective functioning of the SSBT is premised on persistent price differences between soft drink prices based on sugar content. However, this is barely evident in the hospitality sector in Ireland. A number of recommendations are suggested, including both increasing the SSBT, and increasing it annually in line with inflation.

SMART pass Automatic Deactivation of Subcutaneous Implantable Cardioverter-Defibrillators.

BACKGROUND: The SMART passTM algorithm for subcutaneous implantable cardioverter-defibrillator (S-ICD) prevents inappropriate shocks due to oversensing. The mechanisms and significance of SMART pass deactivation remain unclear. OBJECTIVE: To assess whether SMART pass deactivation is associated with inappropriate shocks and elucidate the underlying mechanism. METHODS: We retrospectively investigated 115 patients who underwent S-ICD implantation between 2016 and 2021. SMART pass deactivation and inappropriate shocks during follow-up were assessed. The QRS amplitudes of the subcutaneous (S-ECG) and 12-lead electrocardiogram (ECG) at the time of implantation (pre) and SMART pass deactivation (post) were measured. The patients were divided into the SP-ON group with SMART pass consistently on and the SP-OFF group with the experience of SMART pass deactivation. RESULTS: Three of twelve patients in the SP-OFF group experienced inappropriate shocks during a median of 1094 (IQR, 887-1502)-day follow-up compared with four of 87 patients in the SP-ON group. Pre- and post-S-ECG QRS amplitude were significantly lower in the SP-OFF than in the SP-ON group (both, p < 0.05), despite similar 12-lead ECG QRS amplitude in both groups. A significant temporary drop in the QRS amplitude of the S-ECG led to SMART pass deactivation (pre vs. at deactivation p = 0.015, 95% confidence interval: 0.3-1.9). CONCLUSION: The rate of inappropriate shocks was numerically higher following SMART pass deactivation. A low QRS amplitude in S-ECG was a potential risk factor for SMART pass deactivation. Careful follow-up and suitable management are required for managing patients with risks of SMART pass deactivation.

An observational study of therapeutic procedures and in-hospital outcomes among patients admitted for acute myocardial infarction in Spain, 2016-2022: the role of diabetes mellitus.

BACKGROUND: We used the Spanish national hospital discharge data from 2016 to 2022 to analyze procedures and hospital outcomes among patients aged ≥ 18 years admitted for ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) according to diabetes mellitus (DM) status (non-diabetic, type 1-DM or type 2-DM). METHODS: We built logistic regression models for STEMI/NSTEMI stratified by DM status to identify variables associated with in-hospital mortality (IHM). We analyzed the effect of DM on IHM. RESULTS: Spanish hospitals reported 201,950 STEMIs (72.7% non-diabetic, 0.5% type 1-DM, and 26.8% type 2-DM; 26.3% female) and 167,285 NSTEMIs (61.6% non-diabetic, 0.6% type 1-DM, and 37.8% type 2-DM; 30.9% female). In STEMI, the frequency of percutaneous coronary intervention (PCI) increased among non-diabetic people (60.4% vs. 68.6%; p < 0.001) and people with type 2-DM (53.6% vs. 66.1%; p < 0.001). In NSTEMI, the frequency of PCI increased among non-diabetic people (43.7% vs. 45.7%; p < 0.001) and people with type 2-DM (39.1% vs. 42.8%; p < 0.001). In NSTEMI, the frequency of coronary artery by-pass grafting (CABG) increased among non-diabetic people (2.8% vs. 3.5%; p < 0.001) and people with type 2-DM (3.7% vs. 5.0%; p < 0.001). In the entire population, lower IHM was associated with undergoing PCI (odds ratio [OR] [95% confidence interval] = 0.34 [0.32-0.35] in STEMI; 0.24 [0.23-0.26] in NSTEMI) or CABG (0.33 [0.27-0.40] in STEMI; 0.45 [0.38-0.53] in NSTEMI). IHM decreased over time in STEMI (OR = 0.86 [0.80-0.93]). Type 2-DM was associated with higher IHM in STEMI (OR = 1.06 [1.01-1.11]). CONCLUSIONS: PCI and CABG were associated with lower IHM in people admitted for STEMI/NSTEMI. Type 2-DM was associated with IHM in STEMI.

PASS Theory of Intelligence and Its Measurement Using the Cognitive Assessment System, 2nd Edition.

The goal of this paper was to describe the context within which the PASS theory of intelligence was conceived and the reasons why this theory was used to guide the construction of the Cognitive Assessment System and the several versions of the Cognitive Assessment System, 2nd Edition. We also discuss validity issues such as equitable assessment of intelligence, using PASS scores to examine a pattern of strengths and weaknesses related to academic variability and diagnosis, and the utility of PASS scores for intervention. We provide summaries of the research that informs our suggestions that intelligence testing should be theory-based, not constrained by the seminal work of test developers in the early 1900s, and neurocognitive processes should be measured based on brain function.

Detecting pulmonary malignancy against benign nodules using noninvasive cell-free DNA fragmentomics assay.

BACKGROUND: Early screening using low-dose computed tomography (LDCT) can reduce mortality caused by non-small-cell lung cancer. However, ∼25% of the 'suspicious' pulmonary nodules identified by LDCT are later confirmed benign through resection surgery, adding to patients' discomfort and the burden on the healthcare system. In this study, we aim to develop a noninvasive liquid biopsy assay for distinguishing pulmonary malignancy from benign yet 'suspicious' lung nodules using cell-free DNA (cfDNA) fragmentomics profiling. METHODS: An independent training cohort consisting of 193 patients with malignant nodules and 44 patients with benign nodules was used to construct a machine learning model. Base models using four different fragmentomics profiles were optimized using an automated machine learning approach before being stacked into the final predictive model. An independent validation cohort, including 96 malignant nodules and 22 benign nodules, and an external test cohort, including 58 malignant nodules and 41 benign nodules, were used to assess the performance of the stacked ensemble model. RESULTS: Our machine learning models demonstrated excellent performance in detecting patients with malignant nodules. The area under the curves reached 0.857 and 0.860 in the independent validation cohort and the external test cohort, respectively. The validation cohort achieved an excellent specificity (68.2%) at the targeted 90% sensitivity (89.6%). An equivalently good performance was observed while applying the cut-off to the external cohort, which reached a specificity of 63.4% at 89.7% sensitivity. A subgroup analysis for the independent validation cohort showed that the sensitivities for detecting various subgroups of nodule size (<1 cm: 91.7%; 1-3 cm: 88.1%; >3 cm: 100%; unknown: 100%) and smoking history (yes: 88.2%; no: 89.9%) all remained high among the lung cancer group. CONCLUSIONS: Our cfDNA fragmentomics assay can provide a noninvasive approach to distinguishing malignant nodules from radiographically suspicious but pathologically benign ones, amending LDCT false positives.

Modeling of Gamma Index for Prediction of Pretreatment Quality Assurance in Stereotactic Body Radiation Therapy of the Liver.

Purpose: The purpose of this study was to develop a predictive model to evaluate pretreatment patient-specific quality assurance (QA) based on treatment planning parameters for stereotactic body radiation therapy (SBRT) for liver carcinoma. Materials and Methods: We retrospectively selected 180 cases of liver SBRT treated using the volumetric modulated arc therapy technique. Numerous parameters defining the plan complexity were calculated from the DICOM-RP (Radiotherapy Plan) file using an in-house program developed in MATLAB. Patient-specific QA was performed with global gamma evaluation criteria of 2%/2 mm and 3%/3 mm in a relative mode using the Octavius two-dimensional detector array. Various statistical tests and multivariate predictive models were evaluated. Results: The leaf speed (MILS) and planning target volume size showed the highest correlation with the gamma criteria of 2%/2 mm and 3%/3 mm (P < 0.05). Degree of modulation (DoM), MCSSPORT, leaf speed (MILS), and gantry speed (MIGS) were predictors of global gamma pass rate (GPR) for 2%/2 mm (G22), whereas DoM, MCSSPORT, leaf speed (MILS) and robust decision making were predictors of the global GPR criterion of 3%/3 mm (G33). The variance inflation factor values of all predictors were <2, indicating that the data were not associated with each other. For the G22 prediction, the sensitivity and specificity of the model were 75.0% and 75.0%, respectively, whereas, for G33 prediction, the sensitivity and specificity of the model were 74.9% and 85.7%%, respectively. Conclusions: The model was potentially beneficial as an easy alternative to pretreatment QA in predicting the uncertainty in plan deliverability at the planning stage and could help reduce resources in busy clinics.

Highly efficient synthesis of isoxazolones and pyrazolones using g-C3N4·OH nanocomposite with their in silico molecular docking, pharmacokinetics and simulation studies.

An environmentally friendly, versatile multicomponent reaction for synthesizing isoxazol-5-one and pyrazol-3-one derivatives has been developed, utilizing a freshly prepared g-C3N4·OH nanocomposite as a highly efficient catalyst at room temperature in aqueous environment. This innovative approach yielded all the desired products with exceptionally high yields and concise reaction durations. The catalyst was well characterized by FT-IR, XRD, SEM, EDAX, and TGA/DTA studies. Notably, the catalyst demonstrated outstanding recyclability, maintaining its catalytic efficacy over six consecutive cycles without any loss. The sustainability of this methodology was assessed through various eco-friendly parameters, including E-factor and eco-score, confirming its viability as a green synthetic route in organic chemistry. Additionally, the gram-scale synthesis verifies its potential for industrial applications. The ten synthesized compounds were also analyzed via a PASS online tool to check their several pharmacological activities. The study is complemented by in silico molecular docking, pharmacokinetics, and molecular dynamics simulation studies. These studies discover 5D as a potential candidate for drug development, supported by its favorable drug-like properties, ADMET studies, docking interaction, and stable behavior in the protein binding cavity.