The environmental impact of heavy metals in sediments of main valleys in the eastern side of Mosul City, Iraq.

Analyzing the geochemical changes in stream sediments can reveal important surface processes on Earth, like weathering, transportation, and cation exchange. The study area is located on the eastern side of Mosul, where valleys named Al-Rashediya, Al-Kharrazi, Al-Khosar, Al-Danffilli, and Al-Shor flow towards the Tigris River. These valleys' sediments contain diverse components like clay minerals, organic matter, iron oxides, carbonates, and heavy metals (H.M.s), either as part of these substances or adsorbed onto them. In this study, 36 sediment samples were gathered from these valleys. They underwent chemical analysis through X-ray fluorescence to ascertain their chemical constituents of major oxides. To understand the distribution of H.M.s in these sediments, correlation coefficient analysis and factor analysis were utilized. The study employed the geoaccumulation index (Igeo) and enrichment factor (E.F.) to evaluate sediment contamination. The results of Igeo ranged from Cr = 0.24 to 1.83, Ni = -0.92 to 0.77, Cu = -2.41 to 0.05, Zn = -1.83 to 0.89, Pb = -1.54 to 0.36, and As = -2.84 to 0.80. These findings suggest that the valley sediments are generally in the range of deficiency to minimal enrichment and moderate enrichment. However, Al-Danffilli Valley shows strong contamination levels for Cu, Zn, and Pb. The E.F. values for Cr = 3.63-12.50, Ni = 1.95-4.19, Cu = 0.69-12.36, Zn = 1.08-16.19, Pb = 1.25-62.16, and As = 0.60-1.79 indicate levels ranging from deficiency to minimal and moderate enrichment. Al-Danffilli Valley, in particular, was identified as ranging from moderate to extremely high enrichment, attributed to its location near industrial areas and its tributaries.

Artificial intelligence in cancer diagnosis: Opportunities and challenges.

Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.

Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems.

Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation.

SARS-CoV-2 Syncytium under the Radar: Molecular Insights of the Spike-Induced Syncytia and Potential Strategies to Limit SARS-CoV-2 Replication.

SARS-CoV-2 infection induces non-physiological syncytia when its spike fusogenic protein on the surface of the host cells interacts with the ACE2 receptor on adjacent cells. Spike-induced syncytia are beneficial for virus replication, transmission, and immune evasion, and contribute to the progression of COVID-19. In this review, we highlight the properties of viral fusion proteins, mainly the SARS-CoV-2 spike, and the involvement of the host factors in the fusion process. We also highlight the possible use of anti-fusogenic factors as an antiviral for the development of therapeutics against newly emerging SARS-CoV-2 variants and how the fusogenic property of the spike could be exploited for biomedical applications.

Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning.

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

Multidisiplinary design optimization of a power generation system based on waste energy recovery from an internal combustion engine using organic Rankine cycle and thermoelectric generator.

The research paper mainly deals with waste heat recovery from internal combustion engines (ICE) using the organic Rankine cycle (ORC) and Thermoelectric generator (TEG). Simultaneously recovering the wasted heat of both exhaust gases and coolant, a novel configuration named two-stage is proposed. Then a comprehensive thermo-economic analysis and optimization are conducted. Produced power and total cost rate are selected as the objective function of the optimization. Also, the first and second stage pressures of the ORC system are considered as decision variables. Finally, a sensitivity analysis is performed to study the effect of expander inlet temperature, pumps isentropic efficiency, and expander isentropic efficiency on the objective function.

Attenuation hotspots in neurotropic human astroviruses.

During the last decade, the detection of neurotropic astroviruses has increased dramatically. The MLB genogroup of astroviruses represents a genetically distinct group of zoonotic astroviruses associated with gastroenteritis and severe neurological complications in young children, the immunocompromised, and the elderly. Using different virus evolution approaches, we identified dispensable regions in the 3' end of the capsid-coding region responsible for attenuation of MLB astroviruses in susceptible cell lines. To create recombinant viruses with identified deletions, MLB reverse genetics (RG) and replicon systems were developed. Recombinant truncated MLB viruses resulted in imbalanced RNA synthesis and strong attenuation in iPSC-derived neuronal cultures confirming the location of neurotropism determinants. This approach can be used for the development of vaccine candidates using attenuated astroviruses that infect humans, livestock animals, and poultry.

Aquatic toxicity of hydroquinone and catechol following metal oxide treatment to Ceriodaphnia dubia and Pimephales promelas.

Metal oxides comprise a large group of chemicals used in water treatment to adsorb organic pollutants. The ability of titanium dioxide (TiO2) and iron (III) oxide (Fe2O3) to reduce the chronic toxicity of (phenolic) C6H6(OH)2 isomers, namely hydroquinone (HQ) and catechol (CAT) to Ceriodaphnia dubia and Pimephales promelas (less than 24 h-old) were investigated. The toxic endpoints following metal oxide treatment were compared to endpoints of untreated CAT and HQ. In chronic toxicity testing, HQ resulted in greater toxicity than CAT for both test organisms; the median lethal concentrations (LC50) for CAT were 3.66 to 12.36 mg.L-1 for C. dubia and P. promelas, respectively, while LC50 for HQ were 0.07 to 0.05 mg.L-1, respectively. Although both treated solutions presented lower toxic endpoints than those in the untreated solutions, Fe2O3 had a better potential to reduce the toxic effects of CAT and HQ than TiO2.

Cross-Linking Mass Spectrometry Uncovers Interactions Between High-Density Lipoproteins and the SARS-CoV-2 Spike Glycoprotein.

High-density lipoprotein (HDL) levels are reduced in patients with coronavirus disease 2019 (COVID-19), and the extent of this reduction is associated with poor clinical outcomes. While lipoproteins are known to play a key role during the life cycle of the hepatitis C virus, their influence on coronavirus (CoV) infections is poorly understood. In this study, we utilize cross-linking mass spectrometry (XL-MS) to determine circulating protein interactors of the severe acute respiratory syndrome (SARS)-CoV-2 spike glycoprotein. XL-MS of plasma isolated from patients with COVID-19 uncovered HDL protein interaction networks, dominated by acute-phase serum amyloid proteins, whereby serum amyloid A2 was shown to bind to apolipoprotein (Apo) D. XL-MS on isolated HDL confirmed ApoD to interact with SARS-CoV-2 spike but not SARS-CoV-1 spike. Other direct interactions of SARS-CoV-2 spike upon HDL included ApoA1 and ApoC3. The interaction between ApoD and spike was further validated in cells using immunoprecipitation-MS, which uncovered a novel interaction between both ApoD and spike with membrane-associated progesterone receptor component 1. Mechanistically, XL-MS coupled with data-driven structural modeling determined that ApoD may interact within the receptor-binding domain of the spike. However, ApoD overexpression in multiple cell-based assays had no effect upon viral replication or infectivity. Thus, SARS-CoV-2 spike can bind to apolipoproteins on HDL, but these interactions do not appear to alter infectivity.

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.

Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states may be investigated for this purpose. In this preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals is developed for spatially localized and accurate detection of human drowsiness during driving tasks. Methods: This pBCI modality acquired electrophysiological patterns of 12 healthy subjects from the prefrontal (PFC), frontal (FC), and occipital cortices (OC) of the brain. Neurological states are recorded using six EEG channels spread over the right and left hemispheres in the PFC, FC, and OC of the sleep-deprived subjects during simulated driving tasks. In post-hoc analysis, spectral signatures of the δ, θ, α, and ß rhythms are extracted in terms of spectral band powers and their ratios with a temporal correlation over the complete span of the experiment. Minimum redundancy maximum relevance, Chi-square, and ReliefF feature selection methods are used and aggregated with a Z-score based approach for global feature ranking. The extracted drowsiness attributes are classified using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, and ensemble classifiers. The binary classification results are reported with confusion matrix-based performance assessment metrics. Results: In inter-classifier comparison, the optimized ensemble model achieved the best results of drowsiness classification with 85.6% accuracy and precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews correlation coefficient, 70.2% Cohen's kappa score, and 91% area under the receiver operating characteristic curve with 76-ms execution time. In inter-channel comparison, the best results were obtained at the F8 electrode position in the right FC of the brain. The significance of all the results was validated with a p-value of less than 0.05 using statistical hypothesis testing methods. Conclusions: The proposed scheme has achieved better results for driving drowsiness detection with the accomplishment of multiple objectives. The predictor importance approach has reduced the feature extraction cost and computational complexity is minimized with the use of conventional machine learning classifiers resulting in low-cost hardware and software requirements. The channel selection approach has spatially localized the most promising brain region for drowsiness detection with only a single EEG channel (F8) which reduces the physical intrusiveness in normal driving operation. This pBCI scheme has a good potential for practical applications requiring earlier, more accurate, and less disruptive drowsiness detection using the spectral information of EEG biosignals.

Target-AMP: Computational prediction of antimicrobial peptides by coupling sequential information with evolutionary profile.

Antimicrobial peptides (AMPs) are gaining a lot of attention as cutting-edge treatments for many infectious disorders. The effectiveness of AMPs against bacteria, fungi, and viruses has persisted for a long period, making them the greatest option for addressing the growing problem of antibiotic resistance. Due to their wide-ranging actions, AMPs have become more prominent, particularly in therapeutic applications. The prediction of AMPs has become a difficult task for academics due to the explosive increase of AMPs documented in databases. Wet-lab investigations to find anti-microbial peptides are exceedingly costly, time-consuming, and even impossible for some species. Therefore, in order to choose the optimal AMPs candidate before to the in-vitro trials, an efficient computational method must be developed. In this study, an effort was made to develop a machine learning-based classification system that is effective, accurate, and can distinguish between anti-microbial peptides. The position-specific-scoring-matrix (PSSM), Pseudo Amino acid composition, di-peptide composition, and combination of these three were utilized in the suggested scheme to extract salient aspects from AMPs sequences. The classification techniques K-nearest neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) were employed. On the independent dataset and training dataset, the accuracy levels achieved by the suggested predictor (Target-AMP) are 97.07% and 95.71%, respectively. The results show that, when compared to other techniques currently used in the literature, our Target-AMP had the best success rate.

Cranial base parameters in adults with skeletal class I and class II skeletal pattern.

OBJECTIVE: Cranial base parameters exhibit wide variations. This study evaluated cranial base morphological characteristics of class II and class I malocclusions to identify risk factors for class II skeletal malocclusions. METHODS: In this cross-sectional study, we recruited 30 class I adults and 30 class II adults and collected their lateral cephalograms. The cranial base length was calculated by measuring the base of the skull by determining the length of sella-to-nasion, basion to pterygomaxillary fissure, and pterygomaxillary fissure to point A. The cranial base angle was measured by the angle formed by the basion, sella, and nasion, and the base of the angle, which connects the basion and nasion, was measured. RESULTS: The independent t-test for combined values showed no significant differences in one angular and five linear measures between groups. However, one angular measurement was positively correlated when men and women in class I and class II groups were analyzed separately. CONCLUSION: Male patients with class II patterns exhibited larger cranial base angles than did those with class I patterns. Our study suggested that cranial base features have a minimal role in the development of class II malocclusions.

Anti-Fungal Drug Anidulafungin Inhibits SARS-CoV-2 Spike-Induced Syncytia Formation by Targeting ACE2-Spike Protein Interaction.

Drug repositioning continues to be the most effective, practicable possibility to treat COVID-19 patients. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters target cells by binding to the ACE2 receptor via its spike (S) glycoprotein. We used molecular docking-based virtual screening approaches to categorize potential antagonists, halting ACE2-spike interactions by utilizing 450 FDA-approved chemical compounds. Three drug candidates (i.e., anidulafungin, lopinavir, and indinavir) were selected, which show high binding affinity toward the ACE2 receptor. The conformational stability of selected docked complexes was analyzed through molecular dynamics (MD) simulations. The MD simulation trajectories were assessed and monitored for ACE2 deviation, residue fluctuation, the radius of gyration, solvent accessible surface area, and free energy landscapes. The inhibitory activities of the selected compounds were eventually tested in-vitro using Vero and HEK-ACE2 cells. Interestingly, besides inhibiting SARS-CoV-2 S glycoprotein induced syncytia formation, anidulafungin and lopinavir also blocked S-pseudotyped particle entry into target cells. Altogether, anidulafungin and lopinavir are ranked the most effective among all the tested drugs against ACE2 receptor-S glycoprotein interaction. Based on these findings, we propose that anidulafungin is a novel potential drug targeting ACE2, which warrants further investigation for COVID-19 treatment.

Evaluation of Antimicrobial Activity, Cytotoxicity, and Phytochemical Composition of Ocimum americanum L. (Lamiaceae).

Background: Herbal plants are a natural source of novel biomolecules used widely in ethnomedicine. The present study was intended to examine the antimicrobial properties, cytotoxicity, and phytoconstituents of Ocimum americanum L., an herb traditionally used by the people of Swahili (Kenya) against microbial infections. Methods: The aerial parts of Ocimum americanum L. were sourced, dried, milled, and extracted using three solvents: aqueous, acetonic, and 70% hydroethanolic. Additionally, fractions of chloroform and ethyl acetate were obtained from all crude extracts of the plant. The antimicrobial property was evaluated using agar well diffusion and microdilution techniques against human opportunistic pathogens including S. aureus, E. coli, and C. albicans. The brine shrimp cytotoxicity test was used to analyze the lethality of the extracts and fractions. Phytochemical screening was used to qualitatively assay the presence of phytoconstituents. Results: The phytochemical assay confirmed the presence of alkaloids, phenols, flavonoids, tannins, saponins, terpenoids, reducing sugars, anthraquinones, and glycosides. The lethality test demonstrated that all the extracts and fractions were toxic against Artemia salina nauplii with LC50 values ranging from 0.59 to 559.71 µg/ml. Chloroformic fraction of the hydroethanolic extract had the highest lethality with an LC50 value of 0.59 µg/ml. Two of the extracts and their fractions displayed antimicrobial activity against the Gram-positive bacteria (B. cereus and S. aureus) and fungus (C. albicans), while the same extracts had no activity against the Gram-negative bacteria (E. coli and K. pneumoniae). The highest antimicrobial activity was seen in the ethyl acetate fraction of the hydroethanolic extract at 250 mg/ml against Bacillus cereus which had an inhibition zone of 26.00 ± 0.00 and MIC value of 62.5 mg/ml. Conclusion: In the current study, we report that Ocimum americanum L. demonstrated moderate antimicrobial activity, contains numerous phytocompounds, and is highly cytotoxic; thus, further research is needed for bioprospecting a novel compound.

SARS-CoV-2 Spike protein activates TMEM16F-mediated platelet procoagulant activity.

Thrombosis of the lung microvasculature is a characteristic of COVID-19 disease, which is observed in large excess compared to other forms of acute respiratory distress syndrome and thus suggests a trigger for thrombosis that is endogenous to the lung. Our recent work has shown that the SARS-CoV-2 Spike protein activates the cellular TMEM16F chloride channel and scramblase. Through a screening on >3,000 FDA/EMA approved drugs, we identified Niclosamide and Clofazimine as the most effective molecules at inhibiting Spike-induced TMEM16 activation. As TMEM16F plays an important role in stimulating the procoagulant activity of platelets, we investigated whether Spike directly affects platelet activation and pro-thrombotic function and tested the effect of Niclosamide and Clofazimine on these processes. Here we show that Spike, present either on the virion envelope or on the cell plasma membrane, promotes platelet activation, adhesion and spreading. Spike was active as a sole agonist or, even more effectively, by enhancing the function of known platelet activators. In particular, Spike-induced a marked procoagulant phenotype in platelets, by enhancing Ca2+ flux, phosphatidylserine externalization on the platelet outer cell membrane, and thrombin generation. Eventually, this increased thrombin-induced clot formation and retraction. Both Niclosamide and Clofazimine blocked this Spike-induced procoagulant response. These findings provide a pathogenic mechanism to explain lung thrombosis-associated with severe COVID-19 infection. We propose that Spike, present in SARS-CoV-2 virions or exposed on the surface of infected cells in the lungs, enhances the effects of inflammation and leads to local platelet stimulation and subsequent activation of the coagulation cascade. As platelet TMEM16F is central in this process, these findings reinforce the rationale of repurposing Niclosamide for COVID-19 therapy.

Design, synthesis, and antibacterial activity of N-(trifluoromethyl)phenyl substituted pyrazole derivatives.

Design and synthesis of N-(trifluoromethyl)phenyl substituted pyrazole derivatives and their potency as antimicrobial agents are described. Several of these novel compounds are effective growth inhibitors of antibiotic-resistant Gram-positive bacteria and prevent the development of biofilms by methicillin-resistant Staphylococcus aureus (MRSA) and Enterococcus faecalis. These compounds eradicated the preformed biofilms effectively and were found to be more effective than the control antibiotic vancomycin. Potent compounds showed low toxicity to cultured human embryonic kidney cells with a selectivity factor of >20. The most promising compound is very potent against meropenem, oxacillin, and vancomycin-resistant clinical isolates of Enterococcus faecium. Investigations into the mode of action by performing macromolecular synthesis inhibition studies showed a broad range of inhibitory effects, suggesting targets that have a global effect on bacterial cell function.

SARS-CoV-2 RNAemia and proteomic trajectories inform prognostication in COVID-19 patients admitted to intensive care.

Prognostic characteristics inform risk stratification in intensive care unit (ICU) patients with coronavirus disease 2019 (COVID-19). We obtained blood samples (n = 474) from hospitalized COVID-19 patients (n = 123), non-COVID-19 ICU sepsis patients (n = 25) and healthy controls (n = 30). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in plasma or serum (RNAemia) of COVID-19 ICU patients when neutralizing antibody response was low. RNAemia is associated with higher 28-day ICU mortality (hazard ratio [HR], 1.84 [95% CI, 1.22-2.77] adjusted for age and sex). RNAemia is comparable in performance to the best protein predictors. Mannose binding lectin 2 and pentraxin-3 (PTX3), two activators of the complement pathway of the innate immune system, are positively associated with mortality. Machine learning identified 'Age, RNAemia' and 'Age, PTX3' as the best binary signatures associated with 28-day ICU mortality. In longitudinal comparisons, COVID-19 ICU patients have a distinct proteomic trajectory associated with mortality, with recovery of many liver-derived proteins indicating survival. Finally, proteins of the complement system and galectin-3-binding protein (LGALS3BP) are identified as interaction partners of SARS-CoV-2 spike glycoprotein. LGALS3BP overexpression inhibits spike-pseudoparticle uptake and spike-induced cell-cell fusion in vitro.

RNA interference therapeutics for cardiac regeneration.

There is an impelling need to develop new therapeutics for myocardial infarction and heart failure. A novel and exciting therapeutic possibility is to achieve cardiac regeneration through the stimulation of the endogenous capacity of cardiomyocytes to proliferate. Proof-of-concept evidence of microRNA-induced cardiac regeneration is available in both small and large animals using viral vectors. However, a clinically more applicable strategy is the development of lipid-mediated nanotechnologies for the administration of RNA therapeutics as synthetic molecules. The recent success of the Stable Nucleic Acid Lipid Particle (SNALP) platform for the generation of nanosized, efficient and non-inflammatory lipid nanoparticles paves the way to the development of injectable nanoformulations of microRNAs through cardiac catheterisation.

Molecular Detection of Staphylococcal Enterotoxins and mecA Genes Products in Selected Food Samples Collected from Different Areas in Khartoum State.

Staphylococcal food poisoning is an intoxication that results from the consumption of improperly prepared or stored foods containing sufficient amounts of one or more preformed S. aureus enterotoxins. Nowadays, many researchers worldwide noted an emergence of resistant strains such as Staphylococci particularly for the antibiotic methicillin. Therefore, this study was aimed to determine the existence of Staphylococcus aureus and its enterotoxins, mecA genes, in selected food samples. A total of 400 selected food samples were collected from different areas in Khartoum State. The selected foods included cheese, meat products, fish, and raw milk. One hundred samples from each type of food were cultivated, and the resultant growth yielded 137 (34.25%) S. aureus, 126 (31.5%) bacteria other than S. aureus, and 137 (34.25%) yielded no growth. Eighty-four of the 137 S. aureus isolates were randomly selected and tested for the presence of mecA and enterotoxin genes. The oxacillin sensitivity test showed that 15 (11%) of 137 S. aureus isolates were oxacillin resistant. The PCR assay showed that the mecA gene was detected in 15 of 84 (17%) S. aureus isolates. Simultaneously, only 2 (2.385%) out of 84 S. aureus isolates showed an enterotoxin B gene product. There was a relatively moderate prevalence of methicillin-resistant Staphylococcus aureus with very low frequency of enterotoxin B gene in different kinds of selected food samples collected from Khartoum State. These findings elucidate the increased risk on public in Khartoum being affected by Staphylococcal food poisoning upon consumption of dairy or meat products prepared in unhygienic conditions that could lead to intoxication by Staphylococcus aureus enterotoxins.