Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning.

Hyperspectral imaging (HSI) has been applied to assess the texture profile analysis (TPA) of processed meat. However, whether the texture profiles of live fish muscle could be assessed using HSI has not been determined. In this study, we evaluated the texture profile of four muscle regions of live common carp by scanning the corresponding skin regions using HSI. We collected skin hyperspectral information from four regions of 387 scaled and live common carp. Eight texture indicators of the muscle corresponding to each skin region were measured. With the skin HSI of live common carp, six machine learning (ML) models were used to predict the muscle texture indicators. Backpropagation artificial neural network (BP-ANN), partial least-square regression (PLSR), and least-square support vector machine (LS-SVM) were identified as the optimal models for predicting the texture parameters of the dorsal (coefficients of determination for prediction (rp) ranged from 0.9191 to 0.9847, and the root-mean-square error for prediction ranged from 0.1070 to 0.3165), pectoral (rp ranged from 0.9033 to 0.9574, and RMSEP ranged from 0.2285 to 0.3930), abdominal (rp ranged from 0.9070 to 0.9776, and RMSEP ranged from 0.1649 to 0.3601), and gluteal (rp ranged from 0.8726 to 0.9768, and RMSEP ranged from 0.1804 to 0.3938) regions. The optimal ML models and skin HSI data were employed to generate visual prediction maps of TPA values in common carp muscles. These results demonstrated that skin HSI and the optimal models can be used to rapidly and accurately determine the texture qualities of different muscle regions in common carp.

Relationships of work stress and interpersonal needs with industrial workers' mental health: a moderated mediation model.

OBJECTIVES: This study explores whether feelings of defeat (i.e., a sense of failed struggle and losing rank; referred to as defeat for simplicity) mediated the effect of work stress on depression/anxiety, the effect of interpersonal needs on depression/anxiety for Chinese industrial workers, and the possible moderating role of social support. METHOD: A cross-sectional study was conducted in Shenzhen, China in 2019, in total, 2023 industrial workers (of 2700 invited; response rate = 75%) completed a self-administered survey consisted of Job Stress Scale, Interpersonal Needs Questionnaire, Defeat Scale, Centre for Epidemiological Studies Depression Scale, Generalized Anxiety Disorder Scale, two face-valid questions for social support, as well as sociodemographic information. Moderated mediation model was tested and loop plots were applied to probe into the conditional effects of work and interpersonal stress on depression and anxiety symptoms. RESULT: Both the direct and indirect effect of work stress on depression and anxiety through defeat were significant (Work stress→ Depression: B = 0.035, p < .001, Work stress→ Defeat→ Depression: B = 0.034, p < .001; Work stress→ Anxiety: B = 0.038, p < .001, Work stress→ Defeat→ Anxiety: B = 0.045, p < .001). Meanwhile, defeat mediated the relationship of interpersonal needs with depression partially and the relationship of interpersonal needs with anxiety totally (Interpersonal needs→ Anxiety: B = 0.133, p < .001, Interpersonal needs→ Defeat→ Anxiety: B = 0.010, p = .537). Social support moderated the indirect path between interpersonal needs and depression/anxiety and buffered the effect. CONCLUSION: The mediating role of defeat and the moderator role of social support in the relationship between stress and depression/anxiety were confirmed in industrial workers. Workers who reported more work and interpersonal stress would report more defeat feelings, and then exhibited more depression and anxiety symptoms; this mediation effect was stronger for those who had lower social support, respectively.

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants.

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

Optimization of key energy and performance metrics for drug product manufacturing.

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors (Sampat et al., 2022), this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. The results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

Covered stent treatment for arterial complications after pancreatic surgery: risk assessment for recurrence and peri-stent implantation management.

OBJECTIVES: To explore the risk factors for recurrence of arterial complications after pancreatectomy during the period of covered stent implantation and to provide some opinions on peri-stent implantation management. METHODS: Data on patients implanted with covered stents due to arterial complications after pancreatectomy between January 2017 and December 2021 were analyzed retrospectively. Technical success, clinical success, recurrence, and survival were evaluated to elucidate the practicability of covered stents. Wilson score, Random Forest, logistic regression, and Pearson's chi-square test with bootstrap aggregation were performed for determining the perioperative risk factors for recurrence. RESULTS: Among all fifty-five patients, success stent implantation (technical success) was achieved 100%. Patients who were hemodynamically stabilized without further treatment for artery complications in situ (clinical success) accounted for 89.1%. Based on statistical analysis, pre-stent implantation pancreatic fistula was identified as a robust recurrence-related risk factor for preoperative assessment (p = 0.02, OR = 4.5, 95% CI [1.2, 16.9]; pbootstrap = 0.02). Post-stent implantation pancreatic fistula (p = 0.01, OR 4.5, 95% CI [1.4, 14.6]; pbootstrap < 0.05) and SMA branches or GDA stumps (p = 0.02, OR 3.4, 95% CI [1.1, 10.3]) were relevant to recurrence. The survival rate during hospitalization was 87.3%. All survivors were free from recurrence during the subsequent follow-up. Vasospasm and stent occlusion were observed as short-term and long-term complications, respectively. CONCLUSION: A covered stent implantation is a feasible and effective treatment option for post-pancreatectomy arterial complications. Rigorous management of pancreatic fistula, timely detection of problems, sensible strategies during stent implantation, and reasonable anticoagulation therapy are necessary for a better prognosis. KEY POINTS: • A covered stent is feasible for various artery-related complications after pancreatectomy and has an ideal therapeutic effect. • Pancreatic fistula during the perioperative period of the covered stent is an independent risk factor for recurrent arterial complications and SMA branches or GDA stumps are prone to be recurrent offending arteries. • Rigorous management of pancreatic fistula, timely detection of problems, sensible strategies during stent implantation, and reasonable anticoagulation therapy are necessary for a better prognosis.

Assessment of acute pancreatitis severity via determination of serum levels of hsa-miR-126-5p and IL-6.

Early assessment of acute pancreatitis (AP) severity is key to its treatment. The present study aimed to explore the role of microRNAs (miRNAs/miRs) combined with inflammatory factors in determining AP severity. For this, serum pro-inflammatory cytokines [tumor necrosis factor (TNF)-α, interleukin (IL)-1, IL-6, IL-8 and IL-10)] and miRNAs [Homo sapiens (hsa)-miR-548d-5p, hsa-miR-126-5p and hsa-miR-130b-5p] were detected in patients with mild AP (MAP), severe AP (SAP) and recurrent AP (RAP). High expression of IL-10, TNF-α, hsa-miR-126-5p, hsa-miR-548d-5p and hsa-miR-130b-5p was able to distinguish SAP from MAP and RAP (P<0.05). Multifactorial binary logistic regression analysis indicated that IL-1/IL-6 combined with hsa-miR-126-5p/hsa-miR-548d-5p had a significant influence on AP and AP severity (P<0.05). Receiver operating characteristic analysis revealed that IL-1 combined with hsa-miR-126-5p [area under the curve (AUC), 0.926; sensitivity, 90.0%; specificity, 86.7%, P<0.001] and IL-6 combined with hsa-miR-126-5p (AUC, 0.952; sensitivity, 93.3%; specificity, 90.0%; P<0.001) were able to better distinguish MAP from SAP than IL-1/IL-6 combined with hsa-miR-548d-5p, lipase, and amylase. IL-1 or IL-6 combined with hsa-miR-548d-5p (AUC, 0.924; sensitivity, 83.3%; specificity, 93.3%; P<0.001) were able to better distinguish SAP from RAP than IL-1/IL-6 combined with hsa-miR-126-5p, lipase, and amylase. IL-1 combined with hsa-miR-126-5p (AUC, 0.926; sensitivity, 90.0%; specificity, 86.7%; P<0.001) and IL-6 combined with hsa-miR-126-5p (AUC, 0.952; sensitivity, 93.3%; specificity, 90.0%; P<0.001) were able to better differentiate between MAP and RAP than IL-1/IL-6 combined with hsa-miR-548d-5p, lipase, and amylase. These results demonstrated that the combined detection of serum IL-6 and hsa-miR-126-5p may be useful for the early prediction of AP classification.

Trace metals in aquatic environments of a mangrove ecosystem in Nansha, Guangzhou, South China: pollution status, sources, and ecological risk assessment.

Mangrove forests are widely located along coastlines. They have been identified to be inimitable and dynamic ecosystems. This study investigated the trace metals in mangrove water and surface sediments of Nansha, Guangzhou, China. Zn (148.42 ± 247.47 µg L-1) was the most abundant metal in waters, followed by As (82.34 ± 118.95 µg L-1), Pb (22.96 ± 120.50 µg L-1), and Ni (19.42 ± 47.84 µg L-1). In sediments, the most abundant metal was Fe (27.04 ± 1.91 g kg-1), followed by Mn (1049.04 ± 364.11 mg kg-1), Zn (566.33 ± 244.37 mg kg-1), and Cr (106.9 ± 28.51 mg kg-1). Higher contents of trace metals were detected in vicinity areas of the river mouth. The results of pollution indexes, including contamination factor, enrichment factor, and geo-accumulation index, indicated the pollution of Cd, Cu, Pb, and Zn in sediments. The Spearman correlation and cluster analysis were used to evaluate the metal sources. In water, the significant correlations among Zn and water chemical parameters (Na, Mg, K, Ca, conductivity, pH, and Cl) might indicate the natural source of Zn from the seawater. Water sampling sites in estuaries and coastal areas were clustered separately, which might indicate the influences of upstream water and the seawater, respectively. In sediments, the significant relationships among Cd, Pb, and Zn concentrations were likely to imply the emissions from industries and exploitation of the Pb-Zn mine. The occurrence of Cr and Cu in sediments can be attributed to the spills of lubricants or oil. Cd in sediments could cause serious ecological risk.