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Abstract There is an urgent need for preventive and therapeutic drugs to effectively treat and prevent viral diseases from resurfacing as they emerge during the COVID-19 pandemic. This study aims to assess the antiviral effects of four natural compounds commonly used in traditional medicine to treat SARS-CoV-2 infection. A cytotoxicity, dose-dependent, and plaque reduction assay was performed on Vero CCL-81 cells to figure out their effects on the cells. Quantification of cytokines was assessed. In silico analysis for the selected compound was also evaluated. Results revealed that the compounds could disrupt the viral replication cycle through direct inhibition of the virus or immune system stimulation. The cytotoxicity assay results revealed that the compounds were well tolerated by the cells, indicating that the compounds were not toxic to the cells. This study evaluated the antioxidant capacities of propolis, curcumin, quercetin, and ginseng using ABTS, FRAP, and CUPRAC assays, revealing that propolis exhibited the highest antioxidant activity of ABTS with 1250.40 ± 17.10 μmol Trolox eq/g, with FRAP values reaching 1200.55 ± 15.90 μmol Fe2⁺ eq/g and CUPRAC values of 1150.80 ± 14.20 μmol Trolox eq/g at 1000 µg/mL, highlighting its potential as a potent natural antioxidant. The results of the plaque reduction assay revealed that the compounds could reduce the size and number of plaques, indicating that the compounds could inhibit the virus replication cycle. Subsequently, using molecular docking to analyze the effect of propolis, curcumin, quercetin, and ginseng as inhibitors, it was unveiled that the four compounds are likely to have the potential to inhibit the protease activity, spike protein S1, and RNA polymerase of SARS-CoV-2 and the virus titer was reduced by 100% after post-infection using propolis as an inhibitor control. Graphical Abstract

Abstract Background and aim Recurrent pregnancy loss (RPL), also known as recurrent implantation failure (RIF), is a distressing condition affecting women characterized by two or more consecutive miscarriages or the inability to carry a pregnancy beyond 20 weeks. Immunological factors and genetic variations, particularly in Vit D Binding Protein (VDBP), have gained attention as potential contributors to RPL. This study aimed to provide insight into the immunological, genetic, and metabolic networks underlying RPL, placing a particular emphasis on the interactions between VDBP, HLA-G1, CTLA-4, ENTPD1, and adenosine-fetal-maternal conflict crosstalk. Methods A retrospective study included 198 women with three or more consecutive spontaneous abortions. Exclusion criteria comprised uterine abnormalities, endocrine disorders, parental chromosomal abnormalities, infectious factors, autoimmune diseases, or connective tissue diseases. Immunological interplay was investigated in 162 female participants, divided into two groups based on their Vit D levels: normal Vit D-RPL and low Vit D-RPL. Various laboratory techniques were employed, including LC/MS/MS for Vit D measurement, ELISA for protein detection, flow cytometry for immune function analysis, and molecular docking for protein–ligand interaction assessment. Results General characteristics between groups were significant regarding Vit D and glucose levels. Low Vit D levels were associated with decreased NK cell activity and downregulation of HLA-G1 and HLA-G5 proteins, while CTLA-4 revealed upregulation. VDBP was significantly downregulated in the low Vit D group. Our findings highlight the intricate relationship between Vit D status and adenosine metabolism by the downregulation of SGLT1, and NT5E, key components of adenosine metabolism, suggests that Vit D deficiency may disrupt the regulation of adenosine levels, leading to an impaired reproductive outcome. HNF1β, a negative regulator of VDBP, was upregulated, while HNF1α, a positive regulator, was downregulated in low Vit D women after RPL. Molecular docking analysis revealed crucial residues involved in the interaction between Vit D and HNF1β. Conclusion Collectively, these findings underscore the importance of Vit D in modulating immune function and molecular pathways relevant to pregnancy maintenance, highlighting the need for further research to elucidate the mechanisms and potential therapeutic interventions for improving pregnancy outcomes in individuals with Vit D deficiency and RPL.

Abstract Background and aim Metabolic dysfunction-associated steatotic liver disease (MASLD) formerly known as non-alcoholic fatty liver disease (NAFLD) is the most common liver condition globally. The FIB-4 test is used to detect fibrosis in fatty liver disease but has limited accuracy in predicting liver stiffness, resulting in high rates of false positives and negatives. The new BAST scoring system, incorporating waist circumference, AST, and BMI, has been developed to assess the presence of fibrosis in NAFLD patients. This study compares the effectiveness of BAST and FIB-4 in predicting liver fibrosis in MASLD patients. Patients and methods The study included 140 non-diabetic MASLD patients who underwent transient elastography measurement. BAST score and FIB-4 were calculated for each patient. Patients were grouped based on fibrosis severity; F1, F2, and F3–F4. The sensitivity and specificity of the BAST score and FIB-4 were assessed using receiver operating characteristic curves. Results The BAST score increased significantly with fibrosis progression from F1 to F3–F4. In differentiating advanced fibrosis (F2–F3) from mild/moderate fibrosis (F1–F2), the BAST score at cutoff ≤ − 0.451 showed better diagnostic performance with 90.70% sensitivity, 74.07% specificity, 84.8% PPV and 83.3% NPV compared to FIB-4 that had 60.47% sensitivity, 50.0% specificity, 65.8% PPV and 44.3% NPV. Similarly, for differentiating between F1 and F2 fibrosis, the BAST score at cutoff ≤ − 1.11 outperformed FIB-4, with 80.23% sensitivity, 79.49% specificity, 89.6% PPV and 64.6% NPV, while FIB-4 had 59.30% sensitivity, 51.28% specificity, 72.9% PPV and 36% NPV. Conclusions The BAST score is a better predictor of liver fibrosis in MASLD compared to FIB-4, especially in cases of advanced fibrosis or cirrhosis.

Abstract Background The neutrophil percentage-to-albumin ratio (NPAR) is a novel measure of systemic inflammation and infection. Low albumin levels increase the risk of infection, while high neutrophil counts indicate the presence of infection. Spontaneous bacterial peritonitis (SBP) is a serious infection in cirrhotic ascites, and the potential of NPAR in diagnosing SBP is not yet established. Objective The objective of this study is to determine the diagnostic value of NPAR in identifying SBP. Patients This prospective multicenter study included 465 patients diagnosed with cirrhotic ascites and SBP according to international guidelines. Demographic, clinical, and laboratory data were collected. The sensitivity and specificity of NPAR values for diagnosing SBP were assessed using the receiver operating characteristic curve. Results For SBP diagnosis in the total cohort, NPAR of > 17 had a sensitivity of 85.71%, specificity of 66.67%, and 95% CI (42.1–99.6). In culture-positive SBP, the NPAR at a cut-off > 5.2 had a sensitivity of 85.71%, specificity of 83.33%, and 95% CI (0.709 to 0.979), while in culture-negative SBP, the NPAR at a cut-off > 2.1 had a sensitivity of 92.86%, specificity of 33.33% and CI (0.367 to 0.764). The multivariate analysis revealed that albumin (OR = 2.78, [1.11;3.98], INR (OR = 0.198, [0.066;0.596], creatinine (OR = 0.292, [0.1; 0.81], CRP (OR = 3.18, [1.239;4.52] total leukocytic count (TLC) (OR = 1.97, [1.878; 2.07], platelets (OR = 2.09, [0.99; 2.31] and neutrophil (OR = 3.43, [1.04;3.89] were significantly associated with higher prediction rates for culture positive SBP. Conclusions NPAR could be a new, affordable, noninvasive test for diagnosing SBP.

Abstract Background Cytokines play a crucial role in regulating the function of the immune system by controlling the production, differentiation, and activity of immune cells. Occult hepatitis C virus (OHCV) infection can lead to liver damage, including cirrhosis and hepatocellular carcinoma. This study investigates the immunopathogenic impact of the cytokines IL-17 and IL-22 in OHCV infection compared to chronic hepatitis C (CHC) infection. Methods We studied three groups of patients: 35 with OHCV, 100 untreated patients with CHC, and 30 healthy control subjects. All subjects underwent physical examination and biochemical testing. We used the sandwich enzyme-linked immunosorbent assay method to measure serum IL-17 and IL-22 levels in all groups. Results Compared to the occult and control groups, the CHC group had significantly higher serum IL-17 levels (p

Abstract Background Sorafenib (Sor) is the only approved multikinase inhibitor indicated for the treatment of HCC. Previous studies have shown that amygdalin (Amy) possesses anticancer activities against several cancer cell lines; we suggested that these compounds might disrupt AMPK/mTOR and BCL-2. Therefore, the current study used integrated in vitro and in silico approaches to figure out Amy and Sor’s possible synergistic activity in targeting AMPK/mTOR and BCL-2 for anti-angiogenesis and apoptosis cell death in HepG2 cells. Results Notably, Amy demonstrated exceptional cytotoxic selectivity against HepG2 cells in comparison to normal WI-38 cells (IC50 = 5.21 mg/ml; 141.25 mg/ml), respectively. In contrast, WI-38 cells were far more sensitive to the toxicity of Sor. A substantial synergistic interaction between Amy and Sor was observed (CI50 = 0.56), which was connected to cell cycle arrest at the S and G2/M stages and increased apoptosis and potential necroptosis. Amy and Sor cotreatment resulted in the highest glutathione levels and induction of pro-autophagic genes AMPK, HGMB1, ATG5, Beclin 1, and LC3, suppressed the mTOR and BCL2 anti-apoptotic gene. Finally, the docking studies proposed that Amy binds to the active site of the AMPK enzyme, thus inhibiting its activity. This inhibition of AMPK ultimately leads to inhibition of mTOR and thus induces apoptosis in the HepG2 cells. Conclusion Although more in vivo research using animal models is needed to confirm the findings, our findings contribute to the evidence supporting Amy’s potential anticancer effectiveness as an alternative therapeutic option for HCC.

Abstract Background and aim There is lack of 30-day hospital readmission prediction score in patients with liver cirrhosis and SBP. The aim of this study is to recognize factors capable of predicting 30-day readmission and to develop a readmission risk score in patients with SBP. Methods This study prospectively examined the 30-day hospital readmission for patients previously discharged with a diagnosis of SBP. Based on index hospitalization variables, a multivariable logistic regression model was implemented to recognize predictors of patient hospital readmission within 30 days. Consequently, Mousa readmission risk score was established to predict 30-day hospital readmission. Results Of 475 patients hospitalized with SBP, 400 patients were included in this study. The 30-day readmission rate was 26.5%, with 16.03% of patients readmitted with SBP. Age ≥ 60, MELD > 15, serum bilirubin > 1.5 mg/dL, creatinine > 1.2 mg/dL, INR > 1.4, albumin

Abstract Environmental contamination by phenol has been reported in both aquatic and atmospheric environments. This study aimed to separate and purify the peroxidase enzyme from bacteria that degrade phenol from wastewater sources. An enrichment culture of MSM was used to screen 25 bacterial isolates from different water samples for peroxidase production, six of the isolates exhibited high levels of peroxidase enzyme activity. Qualitative analysis of peroxidase revealed that isolate No. 4 had the highest halo zones (Poly-R478: 14.79 ± 0.78 mm, Azure B: 8.81 ± 0.61 mm). The promising isolate was identified as Bacillus aryabhattai B8W22 by 16S rRNA gene sequencing with accession number OP458197. As carbon and nitrogen sources, mannitol and sodium nitrate were utilized to achieve maximum peroxidase production. A 30-h incubation period was used with pH 6.0, 30 °C, mannitol, and sodium nitrate, respectively, for maximal production of peroxidase. Purified peroxidase enzyme showed 0.012 U/mg specific activity, and SDS-PAGE analysis indicated a molecular weight of 66 kDa. The purified enzyme exhibits maximum activity and thermal stability at pH values of 4.0 and 8.0, respectively, with maximum activity at 30 °C and complete thermal stability at 40 °C. In the purified enzyme, the Km value was 6.942 mg/ml and the Vmax value was 4.132 mol/ml/hr, respectively. The results demonstrated that Bacillus aryabhattai B8W22 has promising potential for degrading phenols from various phenol-polluted wastewater sources.

Abstract Background Traditional herbal medicine has been used for centuries to cure many pathological disorders, including cancer. Thymoquinone (TQ) and piperine (PIP) are major bioactive constituents of the black seed (Nigella sativa) and black pepper (Piper nigrum), respectively. The current study aimed to explore the potential chemo-modulatory effects, mechanisms of action, molecular targets, and binding interactions after TQ and PIP treatments and their combination with sorafenib (SOR) against human triple-negative breast cancer (MDA-MB-231) and liver cancer (HepG2) cells. Methods We determined drug cytotoxicity by MTT assay, cell cycle, and death mechanism by flow cytometry. Besides, the potential effect of TQ, PIP, and SOR treatment on genome methylation and acetylation by determination of DNA methyltransferase (DNMT3B), histone deacetylase (HDAC3) and miRNA-29c expression levels. Finally, a molecular docking study was performed to propose potential mechanisms of action and binding affinity of TQ, PIP, and SOR with DNMT3B and HDAC3. Results Collectively, our data show that combinations of TQ and/or PIP with SOR have significantly enhanced the SOR anti-proliferative and cytotoxic effects depending on the dose and cell line by enhancing G2/M phase arrest, inducing apoptosis, downregulation of DNMT3B and HDAC3 expression and upregulation of the tumor suppressor, miRNA-29c. Finally, the molecular docking study has identified strong interactions between SOR, PIP, and TQ with DNMT3B and HDAC3, inhibiting their normal oncogenic activities and leading to growth arrest and cell death. Conclusion This study reported TQ and PIP as enhancers of the antiproliferative and cytotoxic effects of SOR and addressed the mechanisms, and identified molecular targets involved in their action.

Abstract The burden of cancer diseases is increasing every year, therefore, the demands to figure out novel drugs that can retain antitumor properties have been raised. This study aimed to investigate the anti-tumor properties of amygdalin (Amy) against Ehrlich ascites carcinoma (EAC) bearing mice and its protective properties against liver damage. Amy and the standard anticancer drug Sorafenib (Sor) were given alone or in combination to Swiss albino female mice that had been injected with EAC cells. Biochemical parameters of liver function (AST, ALT, GGT, total protein, albumin), tumor volume, oxidative stress [malondialdehyde, (MDA)] and antioxidative [superoxide dismutase (SOD), and reduced glutathione (GSH)] markers were measured. The hepatic expression of the antioxidant-related gene [nuclear factor erythroid-2-related factor 2 (Nrf2)], the migration-related gene [matrix metalloprotease 9 (MMP9)], and the angiogenesis-related gene [vascular endothelial growth factor (VEGF)] were evaluated by qPCR. The results revealed that EAC-bearing mice treated with Amy and/or Sor showed a decrease in the tumor burden and hepatic damage as evidenced by (1) decreased tumor volume, number of viable tumor cells; (2) increased number of dead tumor cells; (3) restored the liver function parameters; (4) reduced hepatic MDA levels; (5) enhanced hepatic GSH and SOD levels; (6) upregulated expression of Nrf2; (7) downregulated expression of MMP9 and VEGF, and (8) improved hepatic structure. Among all treatments, mice co-treated with Amy (orally) and Sor (intraperitoneally) showed the best effect. With these results, we concluded that the Amy improved the antitumor effect of Sor and had a protective role on liver damage induced by EAC in mice.

The current study aimed to design and test the accuracy of a variable-rate control metering mechanism (VRCMM) and a remote smart monitoring system (RSMS) for a precision sugarcane transplanter based on IoT technology. The VRCMM is used to operate the seedling metering device at different speeds using a stepper motor based on the travel speed, and the RSMS was employed to evaluate of the three basic parameters of seedling amount, optimum rate, and missed rate. Two types of sensors were used for detecting the sugarcane seedling (SS) and travel speed, including one ultrasonic sensor and one infrared RPM sensor. The study was performed at five travel speeds and four transplant spacings. The findings of laboratory tests showed that the mean record of the relative error between the desired stepper motor speed of the VRCMM and the real value was 3.39%, and it increased with increasing the travel speed. as Additionally, the speed regulation performance was in agreement with the transplanting index. The change in RSMS accuracy is obvious when the travel speed is high and the transplant spacing is small. The RSMS accuracy drops sharply, revealing a leaping change. In conclusion, the smart and intelligent designed sugarcane transplanter would be very useful in sugarcane production.

Accurately estimating crop performance under various fertilizer levels in a rapid and non-destructive manner has become a vital aspect of precision agriculture technology for both economic and environmental benefits. This study aimed to estimate different sugar beet parameters, such as total chlorophyll (Chlt), chlorophyll a (Chla), chlorophyll b (Chlb), root yield (RY), sugar yield (SY), and sugar content (SC) under five nitrogen (N) levels (0, 30, 60, 90, and 120 kg N ha−1). This was achieved by using a combination of the gradient boosting regression (GBR) model with published and newly developed two- and three-band spectral indices (2D- and 3D-SRIs). The results showed that the N levels had the highest proportion of variations (80.4–92.9%) for all parameters, except for SC, which had more variation (59.9%) according to year than the N levels (37.2%). All parameters, except SC, showed a significant increase with gradually increasing N levels. Additionally, the N levels displayed linear and strong positive relationships with the chlorophyll parameters, and linear and strong negative relationships with SC, while these relationships were quadratic and strong with RY and SY. Several published and novel 3D-SRIs exhibited moderate to strong relationships (R2 = 0.65–0.89) with all parameters. The newly developed 3D-SRIs, which involve wavelengths from the visible, near-infrared, and red-edge regions, such as NDI536, 538, 534, NDI738, 750, 542, and NDI448, 734, 398, were effective in accurately estimating all parameters. Combining 2D-SRIs, 3D-SRIs, and the aggregate of all spectral indices (ASRIs) with GBR models could be a robust strategy for estimating the six observed parameters with reasonable precision. The GBR-ASF-6 SRIs and the GBR-ASF-7 SRIs models performed better in predicting Chl content and SC with R2 values of 0.99 and 0.99 (RMSE = 0.073 and 1.568) for the training dataset and R2 values of 0.65 and 0.78 (RMSE = 0.354 and 6.294) for the testing datasets, respectively. The obtained results concluded that published and newly developed 3D-SRIs, GBR based on 2D-SRIs or 3D-SRIs, and the aggregate of all ASRIs can be used in practice to accurately estimate the Chl content, production, and quality of sugar beet across a wide range of N levels under semiarid conditions.

The use of automatic systems in the agriculture sector enhances product quality and the country’s economy. The method used to sort fruits and vegetables has a remarkable impact on the export market and quality assessment. Although manual sorting and grading can be performed easily, it is inconsistent, time-consuming, expensive, and highly influenced by the surrounding environment. In this regard, this study aimed to design and optimize the performance of a low-cost, multi-purpose, automatic RGB color-based sensor for sorting fruits. The proposed automatic color sorting system consists of hardware components including a machine frame, belt and pulleys, conveyor belt, scanning zone, plastic boxes, electric components (stepper motors, RGB color sensors, Arduino Mega, motor drivers), and software components (Arduino IDE version 2.2.1 and C++). Calibration was performed for the light intensity sensor to measure the light intensity inside the scanning zone, the conveyor speed sensor, and the RGB color sensors by testing the RGB color channels. The sensor, the height, conveyor belt color, and light intensity should be carefully adjusted to ensure a high performance of the color-based sorting system. The results showed that the appropriate sensor height ranged from 15 to 30 mm, the optimum color of the conveyor belt was black, and scanning the objects at a light intensity of 25 lux achieved the best output signals. The RGB color sensors achieved an analytical performance similar to that obtained with manual sorting without requiring the use of computers for image processing like other automatic sorting systems do in order to gather RGB data.

Aniseeds (Pimpinella anisum) have gained increasing attention for their nutritional and health benefits. Aniseed extracts are known to contain a range of compounds, including flavonoids, terpenes, and essential oils. These compounds have antimicrobial properties, meaning they can help inhibit the growth of nasty bacteria and other microbes. The purpose of this study was to determine if aniseed extracts have potential antioxidant, phytochemical, and antimicrobial properties against multidrug-resistant (MDR) bacteria. A disc diffusion test was conducted in vitro to test the aniseed methanolic extract’s antibacterial activity. The MIC, MBC, and inhibition zone diameters measure the minimum inhibitory concentration, minimum bactericidal concentration, and size of the zone developed when the extract is placed on a bacterial culture, respectively. HPLC and GC/MS are analytical techniques used for identifying the phenolics and chemical constituents in the extract. DPPH, ABTS, and iron-reducing power assays were performed to evaluate the total antioxidant capacity of the extract. Using HPLC, oxygenated monoterpenes represented the majority of the aniseed content, mainly estragole, cis-anethole, and trans-anethole at 4422.39, 3150.11, and 2312.11 (g/g), respectively. All of the examined bacteria are very sensitive to aniseed’s antibacterial effects. It is thought that aniseed’s antibacterial activity could be attributed to the presence of phenolic compounds which include catechins, methyl gallates, caffeic acid, and syringic acids. According to the GC analysis, several flavonoids were detected, including catechin, isochiapin, and trans-ferulic acid, as well as quercitin rhamnose, kaempferol-O-rutinoside, gibberellic acid, and hexadecadienoic acid. Upon quantification of the most abundant estragole, we found that estragole recovery was sufficient for proving its antimicrobial activity against MDR bacteria. Utilizing three methods, the extract demonstrated strong antioxidant activity. Aniseed extract clearly inhibited MDR bacterial isolates, indicating its potential use as an anti-virulence strategy. It is assumed that polyphenolic acids and flavonoids are responsible for this activity. Trans-anethole and estragole were aniseed chemotypes. Aniseed extracts showed higher antioxidant activity than vitamin C. Future investigations into the compatibility and synergism of aniseed phenolic compounds with commercial antibacterial treatments may also show them to be promising options.

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non–destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits. [ABSTRACT FROM AUTHOR]

Moisture and potassium deficiency are two of the main limiting variables for squash crop performance in many water-stressed places worldwide. If major output decreases are to be avoided, it is critical to detect signs of crop stress as early as possible in the growth cycle. Proximal remote sensing can be a reliable technique for offering a rapid and precise instrument and localized management tool. This study tested the ability of proximal hyperspectral remotely sensed data to predict squash traits in two successive seasons (spring and fall) with varying moisture and potassium rates. Spectral data were collected from drip-irrigated squash that had been treated to varied rates of irrigation and potassium fertilization over both investigated seasons. To forecast potassium-use efficiency (KUE), chlorophyll meter (Chlm), water-use efficiency (WUE), and seed yield (SY) of squash, different commonly used and newly-introduced spectral index values for three bands (3D-SRIs), as well as a Decision Tree (DT) model, were evaluated. The results revealed that the newly constructed three-band SRIs based on the wavelengths of the visible (VIS), near-infrared (NIR), and red-edge regions were sensitive enough to measure the four tested parameters of squash in this study. For instance, NDI558,646,708 presented the highest R2 of 0.75 for KUE, NDI744,746,738 presented the highest R2 of 0.65 for Chlm, and NDI670,628,392 presented the highest R2 of 0.64 for SY of squash. The results further demonstrated that the principal component analysis (PCA) demonstrated the ability to distinguish moisture stress from potassium deficiency stress at the flowering stage onwards. Combining 3D-SRIs, DT-based bands (DT-b), and the aggregate of all spectral characteristics (ASF) with DT models would be an effective strategy for estimating four observed parameters with appropriate accuracy. For example, the model’s approximately 30 spectral characteristics were extremely important for predicting KUE. Its outputs with R2 were, for the training and validation datasets, 0.967 (RMSE = 0.175) and 0.818 (RMSE = 0.284), respectively. For measuring Chlm, the DT-DT-b-20 model demonstrated the best. In the training and validation datasets, the R2 value was 0.993 (RMSE = 0.522) and 0.692 (RMSE = 2.321), respectively. The overall outcomes showed that proximal-reflectance-sensing-based 3D-SRIs and DT models based on 3D-SRIs, DT-b, and ASF could be used to evaluate the four tested parameters of squash under different levels of irrigation regimes and potassium fertilizer.

Global warming is by far the most significant issue caused by climate change. Over the past few decades, heat stress has intensified into a serious issue that has a negative impact on crop production. Hence, it is crucial to modify cultivation systems to cope with this kind of stress, particularly in arid dry regions. In comparison to open-field cultivation, tomato production under protected cultivation techniques in walk-in tunnels that are suited for different farmers’ financial abilities was evaluated during the late summer season. The studied tunnels included a shaded net tunnel with natural ventilation, net tunnel with a fogging system and plastic tunnel with evaporative cooling (wet pad and fans). For the operation of fogging and evaporative cooling systems, solar energy was used as a sustainable, eco-friendly energy source. The results indicated that the solar energy system successfully operated the studied cooling systems. All studied protective cultivation techniques mitigated heat stress on tomato plant and improved the microclimate under walk-in tunnels. Moreover, evaporative cooling and fogging systems significantly increased plant leaf area, cell membrane efficiency and the contents of chlorophyll, relative water and proline compared to the net tunnel with natural ventilation. Furthermore, a marked reduction in physiological disorders was noticed. Improved physiological and biochemical parameters and limited physiological diseases led to higher fruit set, marketable fruit yield and total productivity. The percentage of marketable fruit yield increased by around 31.5% with an evaporative cooling system, 28.8% with a fogging system and 17% with a shaded net tunnel with no positive cooling as compared to an open field. However, the plants grown in open-field cultivation without protection significantly deteriorated from heat stress and had a high incidence of physiological disorders. The most incident physiological disorders were blossom-end rot, cracking, internal white tissues, sunscald, puffiness, blotchy ripening, cat face and exserted stigma. It is recommended to use a solar energy system to modify microclimate conditions through fogging or evaporative cooling under walk-in tunnels to ameliorate heat stress on grown tomato in the late summer season for higher fruit yield and fewer physiological disorders.

Trisomy 18 (T18) and trisomy 21 (T21) are caused by chromosomal abnormalities, leading to intellectual disabilities and various health complications. T18 is caused by an extra chromosome 18, while T21 is caused by an extra chromosome 21. Both conditions cause abnormalities in the genes, which can lead to physical abnormalities and other health complications. Prenatal screening is a critical aspect of prenatal care that aims to identify potential inherited disorders early in pregnancy. Prenatal screening can help in detecting inherited abnormalities, such as T18 and T21, by testing for a certain genetic material in the mother's blood or amniotic fluid. If the genetic material is detected, it can be further tested and, if necessary, a prenatal diagnosis can be made. This allows for early planning, including DNA counseling, to prepare for any potential health complications associated with the disorder. In recent years, microRNAs have emerged as promising candidates for biomarkers in prenatal screening. Traditional screening methods, such as ultrasound, amniocentesis, and chorionic villus sampling, have played a crucial role in identifying trisomy pregnancies, but their accuracy is limited. In this review, we explore some of the anticipated future technological advancements and their anticipated influence on these screening methods. [ABSTRACT FROM AUTHOR]

Water scarcity is a major constraint for food production, particularly in arid and semi-arid environments. In this regard, selecting the best irrigation technique is crucial to overcome water scarcity and enhance water productivity (WP) with no significant yield loss. This study aimed to assess the impact of irrigation techniques of every furrow irrigation (EFI), alternate furrow irrigation (AFI), and drip irrigation (DI), as well as the flow rate, on irrigation system performance parameters, yield, water productivity of cauliflower crop and soil salinity during the two successive growing seasons of 2017/2018 and 2018/2019 under field conditions. The treatments comprised three different irrigation inflow rates: Q1 = 0.47 L/s, Q2 = 0.95 L/s, and Q3 = 1.43 L/s. For both investigated seasons, the AFI + Q3 treatment produced the best water distribution uniformity (DU) and water application efficiency (AE) of 85.10% and 72.73%, respectively, of the surface irrigation, and DI methods across the two growing seasons produced the highest DU of 95%. DI produced the highest cauliflower curd yield (18.12 Mg/fed), followed by EFI + Q3 (12.285 Mg/fed) and AFI + Q3 (11.905 Mg/fed). The maximum mean WP value of 10.6 kg/m3 was recorded with DI, followed by AFI + Q3 (6.24 kg/m3), across the two growing seasons. DI, AFI + Q3, AFI + Q2, AFI + Q1, EFI + Q3, and EFI + Q2 saved irrigation water by 32.63, 28.71, 21.22, 18.04, 10.48, and 3.18%, respectively, compared with EFI + Q1 across the two growing seasons. During both seasons, the average value using the drip irrigation system was 3.60 dS/m. Considering the annual leaching requirements of soil, climate change conditions, and fixed costs, we recommend the use of a drip irrigation system in clayey soil to produce cauliflower, followed by the use of the alternative furrow irrigation method to enable the aeration of the same soil for a lower cost.

Coastal environmental assessment techniques have evolved into one of the most important fields for the long-term development and management of coastal zones. So, the overall aim of the present investigation was to provide effective approaches for making informed decisions about the Gamasa coast sediment quality. Over a two-year investigation, sediment samples were meticulously collected from the Gamasa estuary and littoral shelf. The inductively coupled plasma mass spectra (ICP-MS) was used to the total concentrations of Al, Fe, Ti, Mg, Mn, Cu, P, V, Ba, Cr, Sr, Co, Ni, Zn, Pb, Zr, and Ce. Single elements environmental pollution indices including the geoaccumulation index (Igeo), contamination factor (CF), and enrichment factor (EF), as well as multi-elements pollution indices comprising the potential ecological risk index (RI), degree of contamination (Dc), and pollution load index (PLI) were used to assess the sediment and the various geo-environmental variables affecting the Mediterranean coastal system. Furthermore, the Dc, PLI, and RI were estimated using the random forest (RF) and Back-Propagation Neural Network (BPNN) depending on the selected elements. According to the Dc results, all the investigated sediment samples categories were considerably contaminated. Cr, Co, Ni, Cu, Zr, V, Zn, P, and Mn showed remarkable enrichment in sediment samples and were originated from anthropogenic sources based on the CF, EF, and Igeo data. Moreover, the RI findings revealed that all the samples tested pose a low ecologically risk. Meanwhile, based on PLI, 70% of the Gamasa estuary samples were polluted, while 93.75% of littoral shelf sediment was unpolluted. The BPNNs -PCs-CD-17 model performed the best and demonstrated a better association between exceptional qualities and CD. With R2 values of 1.00 for calibration (Cal.) and 1.00 for validation (Val.). The BPNNs -PCs-PLI-17 models performed the best in terms of measuring PLI with respective R2 values of 1.00 and 0.98 for the Cal. and Val. datasets. The findings showed that the RF and BPNN models may be used to precisely quantify the pollution indices (Dc, PLI, and RI) in calibration (Cal.) and validation (Val.) datasets utilizing potentially toxic elements of surface sediment.