Quality Assessment Research Articles

In-silico approach to characterize the structure and function of a hypothetical protein of Monkeypox virus exploring Chordopox-A20R domain-containing protein activity.

Background: Monkeypox has emerged as a noteworthy worldwide issue due to its daily escalating case count. This illness presents diverse symptoms,including skin manifestations, which have the potential to spread through contact. The transmission of this infectious agent is intricate and readily transfers between individuals.Methods: The hypothetical protein MPXV-SI-2022V502225_00135 strain of monkeypox underwent structural and functional analysis using NCBI-CD Search, Pfam, and InterProScan. Quality assessment utilized PROCHECK, QMEAN, Verify3D, and ERRAT, followed by protein-ligand docking, visualization, and a 100-nanosecond simulation on Schrodinger Maestro.Results: Different physicochemical properties were estimated, indicating a stable molecular weight (49147.14) and theoretical pI (5.62) with functional annotation tools predicting the target protein to contain the domain of Chordopox_A20R domain. In secondary structure analysis, the helix coil was found to be predominant. The three-dimensional (3D) structure of the protein was obtained using a template protein (PDB ID:6zyc.1), which became more stable after YASARA energy minimization and was validated by quality assessment tools like PROCHECK, QMEAN, Verify3D, and ERRAT. Protein-ligand docking was conducted using PyRx 9.0 software to examine the binding and interactions between a ligand and a hypothetical protein, focusing on various amino acids. The model structure, active site, and binding site were visualized using the CASTp server, FTsite, and PyMOL. A 100 nanosecond simulation was performed with ligand CID_16124688 to evaluate the efficiency of this protein.Conclusion: The analysis revealed significant binding interactions and enhanced stability, aiding in drug or vaccine design for effective antiviral treatment and patient management.

An Artificial Intelligence-based Tool to Predict “Unhealthy” Wine and Olive Oil

The application of Artificial Intelligence (AI) in the agri-food industry has witnessed significant advancements, particularly in healthy food, quality assessment, and geographical origin determination of agri-products such as wine and olive oils, whose international market is constantly growing. This paper explores the applicability of Machine Learning (ML) models for predicting “unhealthy” wines and olive oils. As for the healthy factor, two main characteristics of wines and olive oils were evaluated: their quality and their geographical origin. In the study, 3 ML algorithms were compared (Random Forest, Linear Discriminant Analysis, and K-Nearest Neighbors) to predict just by observing their chemical characteristics: i) the quality of red and white wine and ii) the geographical origins of wine and olive oils. Real datasets were used for these case studies. The Synthetic Minority Over-sampling Technique (SMOTE) was used to manage imbalanced data, and well-adopted AI metrics were collected to evaluate the accuracy of the predictions made by the 3 ML algorithms. 3 datasets were analyzed: dataset #01 contains ∼7000 data entries related to 2 types of wine (red and white Portuguese wines) and 11 wine’s chemical characteristics; dataset #02 contains 178 data entries related to 3 different Italian vineyards and 13 wine’s chemical characteristics; dataset #03 contains 572 data entries related to 9 different Italian geographical regions for olive oil and 7 fatty acid characteristics. A total of ∼7700 data points, 5 wines and 9 olive oil types, and 31 chemical characteristics were analyzed. 12 quality metrics were collected per each dataset analysis. The designed ML models achieved good accuracy (>0.65) in predicting the wine quality, a very high accuracy (>0.99) in predicting its vineyard, and a very good accuracy (>0.97) in predicting the origin of olive oil. Moreover, an AI-based tool has been developed that stakeholders can use to predict the quality and geographical origin of wine and olive oil to avoid possible fraudulent and unhealthy product labels. The obtained prediction results showcase AI and ML's potential to help the agri-food industry, providing valuable insights and innovations for producers, distributors, and consumers alike. Integrating AI and ML in the agri-food industry may enhance product quality, consumer trust, and market transparency.

Assessment of Wine Quality, Traceability and Detection of Grapes Wine, Detection of Harmful Substances in Alcohol and Liquor Composition Analysis

Abstract: Wine production is the result of the interaction between various strains and grapes, and its good quality is also affected by many factors. Aureobasidium, Cladosporium, Candida, Filobasidium, Hanseniaspora, Hannaella, Saccharomyces, Wickerhamomyce, Alternaria, Starmerella, Acetobacter, Papiliotrema, Bradyrhizobium, Leuconostoclia, Gluconobacter, Comamonas, and Massilia, are significantly correlated with changes of physiological properties and volatile compounds. Phenolic compounds, shortened as phenolics, are a vital parameter to the quality of wine, and wine phenolics include two main families: non-flavonoids, which consist of hydroxybenzoic acids (HBAs), hydroxycinnamic acids (HCAs), and stilbenes, and flavonoids, comprising flavonols, flavan-3-ols, and anthocyanins. Wine quality is determined by either sensory tests or physicochemical tests, and the latter analyse the wine’s chemical parameters such as sugar, pH, and alcohol level. The most important constituents found in wine are Terpenes; Aldehydes, Pyrazines, Esters, Ketones and diketones, Mercaptans, and Lactones. In wine quality analysis, the most chief variables are volatile acidity, alcohol, sulphates, citric acid, density, total sulfur dioxide, chlorides, pH, fixed acidity, free sulfur dioxide, and residual sugar. Some classifiers utilized for wine quality prediction in machine learning are: k-Nearest Neighbor (KNN), Random Forest, Decision Tree, Support Vector Machines, Linear Regression, Stochastic Gradient Descent, Artificial Neural Networks (ANN), and Naive Bayes. This article is aimed to review wine quality parameters, detection and traceability of wine, and detection of harmful substances in alcohol and liquor composition analysis.

Sex and gender differences in hepatitis C virus risk, prevention, and cascade of care in people who inject drugs: systematic review and meta-analysis

People who inject drugs (PWID) are a priority population in HCV elimination programming. Overcoming sex and gender disparities in HCV risk, prevention, and the cascade of care is likely to be important to achieving this goal, but these have not yet been comprehensively reviewed. Systematic review and meta-analysis. We searched Pubmed, EMBASE and the Cochrane Database of Systematic Reviews 1 January 2012-22 January 2024 for studies of any design reporting sex or gender differences among PWID in at least one of: sharing of needles and/or syringes, incarceration history, injection while incarcerated, participation in opioid agonist treatment or needle and syringe programs, HCV testing, spontaneous HCV clearance, direct-acting antiviral (DAA) treatment initiation or completion, and sustained virological response (SVR). Assessment of study quality was based on selected aspects of study design. Additional data were requested from study authors. Data were extracted in duplicate and meta-analysed using random effects models. PROSPERO registration CRD42022342806. 9533 studies were identified and 92 studies were included. Compared to men, women were at greater risk for receptive needle and syringe sharing (past 6-12 months: risk ratio (RR) 1.12; 95% confidence interval (CI) 1.01-1.23; <6 months: RR 1.38; 95% CI 1.09-1.76), less likely to be incarcerated (lifetime RR 0.64; 95% CI 0.57-0.73) more likely to be tested for HCV infection (lifetime RR 1.07; 95% CI 1.01, 1.14), more likely to spontaneously clear infection (RR1.58; 95% CI 1.40-1.79), less likely to initiate DAA treatment (0.84; 95% CI 0.78-0.90), and more likely to attain SVR after completing DAA treatment (RR 1.02; 95% CI 1.01-1.04). There are important differences in HCV risk and cascade of care indicators among people who inject drugs that may impact the effectiveness of prevention and treatment programming. Developing and assessing the effectiveness of gender-specific and gender-responsive HCV interventions should be a priority in elimination programming. Réseau SIDA-MI du Québec.

Assessment of health-related quality of life and health utility in patients on home parenteral nutrition

Assessment of health-related quality of life and health utility in patients on home parenteral nutrition

Quality improvement of soybean meal by simultaneous microbial fermentation and enzymolysis and untargeted metabolomic analysis of its metabolites

Twelve lactobacillus strains, four yeast strains, and eight proteases were screened, and their effects on oligosaccharide degradation, lactic acid production, and proteolytic capacity were investigated in soybean meal. The simultaneous microbial fermentation and enzymolysis (SMFE) conditions were optimized as feed/water ratio of 1:0.8 (w/v), yeast inoculum of 2.1×105 CFU/g, lactobacillus inoculum of 2.1×107 CFU/g, protease addition of 200 U/g, and fermentation at 37 °C for 48 h. The results showed that SMFE soybean meal exhibited superior performance over unprocessed soybean meal, showing increases in crude protein content from 49.8% to 56.3%, acid-soluble protein content from 3.7% to 10.4%, lactic acid production from 0 to 71.5 g/kg, and a decrease in oligosaccharide content from 6.4% to 0%. The comprehensive performance of SMFE soybean meal is higher than that from enzymolysis or fermentation alone Non-targeted metabolomic analysis revealed a diversity of metabolites in fermented soybean meal produced by different processes, and 8366 detected metabolites were mainly amino acids and their metabolites. Furthermore, among the six experimental groups, the metabolites of the SMFE group, and the yeast and lactic acid bacteria fermentation groups were the most similar. This study facilitates the assessment of overall quality of soybean meal and reveals the superiority of SMFE over traditional methods in enhancing nutritional profile of soybean meal.

Assessment of Nutritional Status and Diet Quality of Female Adolescents in Odeda Local Government, Ogun State

Assessment of Nutritional Status and Diet Quality of Female Adolescents in Odeda Local Government, Ogun State

Protocol for Conducting a Systematic Review on Diagnostic Accuracy in Clinical Research

In the landscape of modern medicine, the ability to accurately diagnose various clinical conditions is paramount. As new diagnostic tools continue to emerge, their accuracy must be rigorously assessed before clinical implementation. This paper introduces a systematic review protocol tailored for diagnostic accuracy studies, drawing inspiration from a review on dysphagia screening in post-stroke patients. The protocol, designed with precision and transparency at its core, facilitates a thorough synthesis of evidence, employing tools such as the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and the Standards for Reporting of Diagnostic Accuracy Studies (STARD) checklist for robust evaluation. The protocol emphasizes registration with the PROSPERO database and adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The systematic search approach encompasses a comprehensive exploration of databases and precise keyword combinations. Distinctive inclusion and exclusion criteria, coupled with a dual-reviewer methodology, ensure the selection of high-quality studies. This framework has the potential to serve as a benchmark for systematic reviews in diagnostic accuracy, highlighting the importance of standardization, transparency, and adaptability in clinical research. This approach paves the way for a research methodology that delves deeper into diagnostic tools across various clinical scenarios, promoting evidence-based advancements in patient care.

Quality assessment standards and production technology of artificial sand based on hydraulic concrete construction specifications

Natural sand and gravel resources are a local resource. Natural sand and gravel are non-renewable in a short time and are not suitable for long-distance transportation. After decades of mining, sand and gravel resources in some areas have been exhausted. The transportation distance of natural sand and gravel is getting farther and farther, and accordingly, the price of natural sand and gravel continues to rise. At the same time, continuous and disorderly mining has caused serious damage to the environment and resources. During the construction of large and medium-sized water conservancy and hydropower projects in China, due to the lack of surrounding natural aggregates, most projects use artificial aggregates. In the current national standards for water conservancy projects, the content of artificial sand and gravel powder is clearly defined. Based on the “Hydraulic Concrete Construction Specifications” (SL677-2014), this article summarizes the particle shape, gradation and surface structure of artificial sand, and also calculates the theoretical gradation of artificial sand. To find out the impact of stone powder content on the performance of artificial sand, different levels of stone powder content need to be selected for research and analysis to clarify the impact of artificial sand powder content on concrete properties. Research shows that for concrete made from artificial sand, increasing the aggregate content with a particle size less than 0.075 mm can significantly increase the compressive strength of the concrete. In artificial sand, the content of stone powder will affect the workability, polishability, durability, permeability and water release of concrete. In the artificial sand and gravel production process system, crushing and sand making equipment are the most important sand and gravel production equipment. The research results provide theoretical reference for the application of artificial sand in concrete and the update of artificial sand production technology and equipment.

Anti-stigma interventions in low-income and middle-income countries: a systematic review

Stigma exacerbates power imbalances and societal disparities, significantly impacting diverse identities and health conditions, particularly for low and middle-income countries (LMICs). Though crucial for dismantling harmful stereotypes, and enhancing healthcare utilisation, existing research on anti-stigma interventions is limited with its condition-focused approach. We aimed to thoroughly evaluate peer-reviewed and non-peer-reviewed literature for a comprehensive review of anti-stigma interventions for diverse identities and all health conditions in LMICs. This review systematically explored peer-reviewed and non-peer-reviewed literature, in ten electronic databases up to January 30, 2024, covering all anti-stigma interventions across various stigmatised identities and health conditions in LMICs. Quality assessment for this systematic review was conducted as per Cochrane Collaboration's suggested inclusions. The review was registered with PROSPERO (Registration: 2017 CRD42017064283). Systematic synthesis of the 192 included studies highlights regional imbalances, while providing valuable insights on robustness and reliability of anti-stigma research. Most studies used quasi-experimental design, and most centred on HIV/AIDS or mental health related stigma, with very little work on other issues. Certain high-population LMICs had no/little representation. The interventions targeted diverse segments of populations and consequently yielded a multitude of stigma-related outcomes. However, despite the heterogeneity of studies, most reported positive outcomes underscoring the effectiveness of existing interventions to reduce stigma. This study is supported by the UK Medical Research Council Indigo Partnership (MR/R023697/1) award.

The Use of Artificial Intelligence for Quality Assessment of Refill Friction Stir Spot Welded Thin Joints

The Use of Artificial Intelligence for Quality Assessment of Refill Friction Stir Spot Welded Thin Joints

Green infant formula analysis: Optimizing headspace solid-phase microextraction of carbonyl compounds associated with lipid peroxidation using GC-MS and pentafluorophenylhydrazine derivatization

The refinement and optimization of a method combining headspace solid-phase microextraction (HS-SPME) with gas chromatography-mass spectrometry (GC-MS) was successfully performed for the first time to determine seven carbonyl and dicarbonyl compounds, including glyoxal, methylglyoxal, dimethylglyoxal, and malondialdehyde in infant formulae, related to lipid peroxidation. HS-SPME was utilized for simultaneous extraction and derivatization with pentafluorophenylhydrazine (PFPH). Critical parameters such as temperature, pH, extractive phase, and salting-out were meticulously investigated and fine-tuned by an asymmetrical 2232//9 screening design to ensure the method's efficacy and reliability. Optimal conditions included a PFPH concentration of 5 g/L, pH 5.0, head-space extraction at 60 °C within 10 min, utilizing a DVB/CAR/PDMS coating, and a 20% w/w salting-out. The analytical validation of this method, compliant with FDA guidelines, demonstrated exceptional linearity, sensitivity, specificity, precision (RSD ≤13.8%), and accuracy (84.8% ≤ recovery ≤111.5%). The metric approach AGREEprep confirms its eco-friendliness, marking a significant step towards an environmentally conscious approach in infant formula analysis. An occurrence study conducted on 25 infant formula samples revealed widespread carbonyl and dicarbonyl compounds in both powdered and liquid variants. ANOVA results exhibited variations in compound concentrations among different sample groups. Clustering analyses delineated distinct groups based on carbonyl content, indicating the potential of these compounds as markers for lipid peroxidation and food quality assessment. This method serves as a valuable tool for evaluating infant formula quality, stability towards oxidation, and safety.

Restarting pre-exposure prophylaxis (PrEP) for HIV: a systematic review and meta-analysis

High coverage of pre-exposure prophylaxis (PrEP) will reduce HIV transmission and help end the HIV/AIDS pandemic. However, PrEP users face challenges, including long-term adherence. The study aimed to document the proportions of individuals who restart HIV PrEP after they stop and the reasons for restarting PrEP. This study is a systematic review and meta-analysis. We systematically searched CINAHL, Embase, Emcare, Global Health, Medline, Scopus, and PsychINFO for peer-reviewed with no date restrictions. A grey literature search was conducted through Google search, a search of abstract books of AIDS conferences and the websites of WHO and UNAIDS. The data search was conducted in April 2023 and updated in February 2024. Two authors extracted data on the proportion of people who stopped and then restarted PrEP, reasons for restarting, and strategies to support people restarting PrEP. Two authors appraised the data using the Joanna Briggs Institute Appraisal Tools. We used a random-effects meta-analysis to pool estimates of restarting. We conducted meta-regression to determine potential sources of heterogeneity. This study is registered with PROSPERO, CRD42023416777. However, we deviated from our original plan as we did not identify enough studies for strategies to support restarting PrEP (primary objective). Subsequently, we revised our plan to strengthen our secondary objective to quantify the proportion of people who stopped and restarted PrEP, and explore possible reasons for its heterogeneity. Of 988 studies, 30 unique studieswere included: 27 reported the proportion restarting PrEP, and of these, 7 also reported reasons for restarting PrEP, and 3 studies reported only on the reasons for restarting PrEP. No study evaluated interventions for restarting PrEP. For the meta-analysis, we included 27 studies. Most studies were from high-income countries (17/27, 63%) or the USA (15/27, 56%). Overall, 23.8% (95% CI: 15.9-32.7, I2=99.8%, N=85,683) of people who stopped PrEP restarted PrEP. There was a lower proportion of restarting in studies from middle-income countries compared to high-income countries (adjusted odds ratio (aOR) 0.6, 95% CI: 0.50-0.73, p<0.001). There was higher restarting in studies from Africa compared to the USA (aOR 1.55, 95% CI: 1.30-1.86), heterosexual populations compared to men who have sex with men or transgender women (aOR 1.50, 95% CI: 1.25-1.81, p<0.001) and in studies defining restarting as those who had stopped PrEP for >1 month compared to those who stopped <1 month (aOR 1.20, 95% CI: 1.06-1.36, p<0.001). Reasons for restarting PrEP included perceived higher risk for HIV acquisition and removal of barriers to access PrEP. In terms of quality assessment, overall, both randomised controlled trials had a low risk of bias, while the observational studies used in the meta-analysis had some potential risk of bias related to not explicitly addressing potential confounders (15/25, 60%) or not describing strategies to address incomplete follow-up (24/25, 96%). About a quarter of people who stopped PrEP would restart, with substantial variation across countries and populations. It is important to understand the motivations and contextual factors influencing restarting PrEP and the support systems to enable restarting PrEP for those at ongoing risk. Australian National Health and Medical Research Council.

Inline Monitoring of continous Ultrasonic Welding Processes of Thermoplastic Composites via a custom polyCMUT based Ultrasound Array

Ultrasonic welding (UW) of thermoplastic composites (TCs) is an emerging technology in the field of composite joining techniques in the aerospace sector. Through a mechanical oscillator, ultrasound at a frequency of 20kHz is induced into the material via a welding horn, where microscopic friction and damping effects melt the thermoplastic. Under further pressure the weld area cools down, permanently joining both parts together. Like all joining processes in the aerospace industry the resulting joints need to be tested for their quality and structural integrity. The traditional testing method using water-coupled ultrasound includes extra steps. This process could be considerably improved by assessing the quality of the weld directly after or even during the welding process, allowing for immediate rework or discard of the parts in question. Ultrasound is still the best solution for this quality assessment, being inexpensive, well understood, and able to create B-Mode images, allowing a look into the cross-section of the weld. However, there are several major problems: To increase the system complexity as little as possible it is necessary to attach the ultrasound unit next to the welding equipment, and as close to the welding horn and compactor as possible to save space and keep the end- effector manoeuvrable. This brings problems for classic piezoelectric ultrasonic arrays: The low welding frequency and its resonance modes reach into the lower resonance modes of the piezoelectric sensors leading to immense noise, hiding any potential echo from the welding zone. Classic piezoelectric crystals are also very brittle and can suffer damage from sustained exposure to this violent environment. The authors present a novel solution: a custom-made polymer-based capacitive micromachined ultrasonic transducer array (polyCMUT). polyCMUTs are tiny drums with two electrodes. One on the bottom and the other suspended over a cavity sandwiched between two layers of polymers. By applying a DC-bias an electrical field is created and the membrane is set under tension. If then an AC voltage is applied, the strength of the electric field decreases, allowing the membrane to snap back into its original position. If done at the resonance frequency of the membrane, a strong ultrasonic signal is created. To receive this signal the polyCMUT is charged with a DC-bias, allowing it to receive the echo of the transmitted signal by measuring the changing capacitance. Not only is the polymer robust and inexpensive to fabricate, the general architecture of CMUTs also allows a design where the first mode of resonance is the actual mode the CMUT is operating in. By designing for a resonance frequency over 5 MHz all noise from the initial welding process is ignored, leading to a working pulse echo imaging system. The array is then mounted onto a PEEK block attached to the compactor unit of the welding end-effector. This publication is intended to present initial results, the design process of the custom array and the tests leading there.

Ecological assessment of pastures semi-deserts and dry steppes of Azerbaijan

The absence in the scientific literature of criteria for assessing the ecological status of pasture lands, insufficient knowledge of the use of predictive methods and technology for carrying out special agrochemical measures, as well as issues of permissible loads, served as the basis for choosing the topic of research work. For the first time, in the conditions of pastures in Azerbaijan, an environmental and energy assessment of soil-landscape complexes was carried out. A detailed and final quality assessment was drawn up on soil scale, and the coefficient of their comparative merit was determined. A scientifically based system of agrochemical measures for the superficial and fundamental improvement of pastures has been developed. The final bonitet scale, reflecting the level of fertility of soil varieties, showed that the soils of the Jeyranchol massif turned out to be the most fertile at 62 points, compared with them, the pasture soils of Ajinohur on average across the massif received 53 points, Gobustan 51 points, and the Kura-Araz lowland 55 points. On average, pasture lands in Azerbaijan are valued at 55 points, which indicates the need for agro-reclamation measures. The types of forage plants are distributed as follows: i) Cereals 116, 12%; ii) Asteraceae 109, 11.2%; iii) Legumes 82, 8.4%; iv) Brassicas 59, 6.2%; 6%; v) Cloves 50, 5.3%; vi) Lamiaceae 42, 4.6%; vii) Linear 40, 4.2%; viii) Goosefoot 6, ix) Gimletaceae 32 species, 3.4%; x) Umbrellas 38, 4.4%; xi) Other 339 types, 35.5%.

Linking Repeated Subjective Judgments and ConvNets for Multimodal Assessment of the Immediate Living Environment

The integration of alternative data extraction approaches for multimodal data, can significantly reduce modeling difficulties for the automatic location assessment. We develop a method for assessing the quality of the immediate living environment by incorporating human judgments as ground truth into a neural network for generating new synthetic data and testing the effects in surrogate hedonic models. We expect that the quality of the data will be less biased if the annotation is performed by multiple independent persons applying repeated trials which should reduce the overall error variance and lead to more robust results. Experimental results show that linking repeated subjective judgements and Deep Learning can reliably determine the quality scores and thus expand the range of information for the quality assessment. The presented method is not computationally intensive, can be performed repetitively and can also be easily adapted to machine learning approaches in a broader sense or be transferred to other use cases. Following aspects are essential for the implementation of the method:•Sufficient amount of representative data for human assessment.•Repeated assessment trials by individuals.•Confident derivation of the effect of human judgments on property price as an approbation for further generation of synthetic data.

Water Quality Assessment in the Ibër River Basin (Kosovo) Using Macroinvertebrate and Benthic Diatom Indices

Water Quality Assessment in the Ibër River Basin (Kosovo) Using Macroinvertebrate and Benthic Diatom Indices

Efficacy of Chemoimmunotherapy versus Chemotherapy for Gastric Cancer: A Meta-Analysis of Survival Outcomes.

Gastric cancer has been traditionally treated with chemotherapy as the primary mode of treatment. However, recent studies have shown that chemoimmunotherapy is also effective and, in some cases, better than chemotherapy treatment. Current study aimed to find the efficacy of chemoimmunotherapy versus chemotherapy in the treatment of gastric cancer. Using electronic databases, including PubMed, Embase, and EBSCO, a thorough literature search was carried out for the years 2006 to 2023. The search strategy was designed to identify relevant studies based on chemoimmunotherapy and chemotherapy intervention, and the search was conducted using appropriate keywords and MeSH terms. The retrieved studies were screened for relevance based on their titles, abstracts, and full texts. The studies' inclusion criteria were predefined, and the selected studies were then subjected to a quality assessment using GradePro GDT. The data from selected studies were extracted and analyzed using Revman version 5.4. The study found that chemoimmunotherapy treatment resulted in a significant improvement in overall survival (OS) with a risk ratio (RR) of 1.54 and a 95% Confidence Interval (CI) of 1.25 to 1.89. The overall effect was also found to be significant, with a p-value of less than 0.001. Furthermore, we also observed an improvement in the 1-year, 3-year, and 5-year survival rates with risk ratio (RR) of 1.09 (95% CI: 1.01, 1.17), 1.43 (95% CI: 1.28, 1.60), and 1.59 (95% CI: 1.10, 2.30), respectively. In addition, it's also found that chemoimmunotherapy treatment also resulted in an improvement in DFS with an RR of 1.94 and a 95% CI of 1.44 to 2.59. Overall, these results suggest that chemoimmunotherapy treatment can be an effective approach in comparison to chemotherapy for improving overall survival and disease-free survival in the studied population. This study comparing chemoimmunotherapy versus chemotherapy for gastric cancer showed that both treatments were effective, but chemoimmunotherapy had more significant efficacy. To support these results, additional studies with a large sample size and a longer follow-up time are required.

Optimal growth conditions to enhance Chlorella vulgaris biomass production in indoor phyto tank and quality assessment of feed and culture Stock

Commercial microalgae cultivation is a dynamic field with ongoing efforts to improve efficiency, reduce costs, and explore new applications. We conducted a study to examine how different light exposure periods affect Chlorella vulgaris's growth. We employed a Phyto tank batch system of approximately 3.5 liters with LED light control, controlled airflow, and sterilized bags, maintained at 22.0 ± 2.0°C indoors. Various methods, including spectrophotometry, and cell counter were employed to monitor Chlorella vulgaris growth under different light exposure cycles. Additionally, quality analysis as feed source was employed by proximate, amino acid, beta-glucan, and microbial content analysis. The results revealed significant variations in C. vulgaris biomass production based on light exposure duration. Notably, the 16:8-hour light-dark photoperiod exhibited the highest biomass concentration, reaching 6.48 × 107 ± 0.50 cells/mL with an optical density (OD) of 1.165 absorbance at 682 nm. The 12:12-hour light-dark photoperiod produced the second-highest biomass concentration, with 2.305 × 106 ± 0.60 cells/mL at an OD of 0.489. Proximate analysis of dry algae powder revealed low lipid content (0.48%), high protein content (37.61%), variable ash concentration (average 10.75%), and a significant carbohydrate fraction (51.16%) during extended daylight and shorter dark periods. Amino acid analysis identified nine essential amino acids, with glutamic acid being the most abundant (17.7%) and methionine the least (0.4%). Furthermore, quality analysis and microbiological assays demonstrated that the C. vulgaris biomass is well-suited for fish and livestock use as a feed source and possibility as human nutraceuticals. These findings can be considered more environmentally friendly and ethically sound due to the absence of genetic modification.

Assessment of the nutritional quality of weaning food prepared from fermented blends of sorghum (Ogi) and soybean

This study investigated the nutritional quality of weaning food prepared from fermented blends of sorghum (ogi) and soybean. The raw samples were processed into flour and were mixed in ratio of 60:40 and 70:30 respectively. Lactic acid bacteria (LAB) were isolated from fermented ogi and were accessed for their technological properties. The pH and total titratable acidity of the ogi was accessed, proximate composition and shelf-life of the weaning blends were determined by standard methods. A total of 10 LAB isolates were obtained, characterized and identified as L. plantarum (50%), L. fermentum (30%), L. delbrueckii (10%), and L. brevi (10%). Lactic acid, diacetyl and hydrogen peroxide produced by the LAB isolates ranged from 1.203 ± 0.006 - 3.410 ± 0.010, 1.21 ± 0.02 - 3.63 ± 0.05 and 0.650 ± 0.060 - 1.910 ± 0.040 (mg/mL) respectively. The fermentation of the ogi resulted in pH reduction from 4.5 ± 0.01- 2.40 ± 0.01 and increased titratable acidity 12.49 ± 0.01 - 23.00±0.01 g/L. The 60:40 weaning blend of sorghum ogi flour and soybean improved protein, ash, crude fiber, moisture, fat, vitamin A, ascorbic acid, riboflavin, and beta carotene content compared to a 70:30 blend. The blend also showed improvements in mineral content, with a significantly higher overall nutritional value compared to the 70:30 blend. Coliforms were not observed during the shelf-life monitoring. This study concluded that these blends meet daily nutritional requirements, provide health benefits, and meet recommended standards for weaning.