IntroductionPatients presenting to the emergency department with a non-ST segment elevation myocardial infarction (NSTEMI) frequently have unremarkable electrocardiography (ECG) reports, alluding to the unreliable nature of ECG in diagnosing NSTEMI. This study aims to assess the burden of NSTEMI in acute coronary syndrome (ACS) with unremarkable ECG, elucidating that in patients presenting with acute retrosternal chest pain, NSTEMI should not be excluded unless cardiac enzyme levels are assessed.MethodsAll patients who fulfilled the inclusion criteria in the Department of Cardiology, Tabba Heart Institute, Karachi were included. After obtaining informed written consent, a detailed history was taken. Clinical examination was consequently performed, and an ECG, along with the cardiac enzymes implicated in ACS, such as troponin I, was evaluated. The proportion of normal ECGs in the context of an NSTEMI was duly noted.ResultA total of 215 patients with ACS presenting within 24 hours of the onset of symptoms, on a background of unremarkable ECG reports, were included. One hundred thirty-eight (64.2%) were males and 77 (35.8%) were females, with the mean age being 54.3 + 7.6 years. A confirmed diagnosis of NSTEMI was made in 49 (22.8%) of the total cases.ConclusionThe frequency of patients presenting with an NSTEMI within 24 hours of the onset of symptoms, and having normal ECG findings, was strikingly high in patients presenting to the Tabba Heart Institute, Karachi, Pakistan. These findings were more common in males and in older patients. Coronary artery disease (CAD) is a global health problem of enormous proportions. It is the leading cause of morbidity and mortality in both men and women worldwide. In 2016, CAD was the leading cause of deaths attributable to cardiovascular disease in the United States (43.2%), followed by stroke (16.9%) and high blood pressure (9.8%) [Acute coronary syndrome (ACS) is a syndrome consisting of signs and symptoms due to decreased blood flow in coronary arteries such that part of the heart muscle is unable to function properly due to hypoperfusion . ACS is Patients with ST-elevation ACS present with acute chest pain and ST-segment elevation on ECG. Non-ST-elevation ACS patients, on the contrary, present with acute chest pain but without ST-segment elevation. ECG usually shows transient or persistent ST-segment depression or T wave inversion or flat T wave or no ECG changes at all. NSTE-ACS is further subdivided into either unstable angina (UA), with normal troponin levels, or non-ST segment elevation myocardial infarction (NSTEMI) with raised troponin levels .Many patients with NSTE-ACS can have normal ECG findings on presentation in the emergency room, showing that ECG may be non-diagnostic in a multitude of ACS cases . PatientUsing a 5% margin of error and 95% level of confidence, the sample size was calculated by using the World Health Organisation (WHO) calculator . ConsecuInclusion criteriaAll ACS patients (as determined by the presence of signs and symptoms such as chest pain), both males and females, between 35 to 65 years, and presenting within 24 hours of the onset of symptoms and having normal ECG reports were included. Normal ECG reports were defined as reports that demonstrated no remarkable features, including ST-segment alterations and/or T wave inversion, amongst other alterations. The included age interval (35-65) was chosen considering that most patients presenting to the Tabba Heart Institute (THI) with ACS fall within that range.Exclusion criteriaKnown cases of valvular heart disease, cardiomyopathy, and congenital heart diseases were excluded. These factors were excluded to ensure that any alterations in the ECG findings were exclusively because of ACS and not, in fact, due to confounding variables such as valvular abnormalities.The data obtained was entered and analysed using the SPSS version 22.0 . Qualitative variables, such as gender, hypertension, diabetes, dyslipidemia, smoking status, obesity, family history, and NSTEMI, were presented as frequencies and percentages. Quantitative variables, such as age and duration of symptoms, were expressed in terms of Mean ± SD. Effect modifiers like age, gender, diabetes, hypertension, smoking status, dyslipidemia, and obesity were addressed through stratification to see the effects of these variables on the outcome. Post-stratification, the Chi-square test was applied; a p-value of 0.05 or less was considered significant.A total of 215 patients, evaluated over one month, with ACS presenting within 24 hours of the onset of symptoms having normal ECG were selected for the purposes of this study. The mean age of the patients was 54.3 + 7.592 years. The distribution of age is presented in Figure 2; the descriptive statistics pertaining to BMI are also presented in Table Of the 215 patients, 138 (64.2%) were males and 77 (35.8%) were females. The mean duration of the symptoms was noted to be 9.166 + 4.500 hours. The distribution of the duration of symptoms is presented in Table The frequencies of age groups, gender, duration of symptoms, DM, HTN, dyslipidemia, obesity, smoking, and a family history positive for MI were calculated; the results are presented in Table ECG is a useful tool for risk stratification of patients who present to the ED with chest pain. Studies during the past two decades have revealed that low-risk patients presenting with chest pain can be identified through a combination of clinical evaluation and ECG at the time of ED presentation -11. GeneMany patients with NSTE-ACS can have unremarkable ECG findings on presentation in the emergency room, showing that ECG may be non-diagnostic in ACS in many cases . PatientIn our study, NSTEMI was seen in 49 patients (22.8%). A study by Turnipseed et al. showed that the frequency of ACS in patients with normal ECG was 17% . StudiesRisk stratification is a crucial step in ACS, with significant implications on patient management and prognosis. Many risk scores and factors have been published in medical literature during the last decade to help attain a more precise clinical decision. Historically, ECG has been pivotal in evaluating electrophysiological disturbances underlying cardiac pathology, but physicians should be wary of their limitations.A study by Teixeira et al., further observed that 22% of patients with a normal ECG went on to develop an NSTEMI, which on a practical level forces the clinician to achieve a precise clinical and biochemical characterisation, considering that a normal ECG may not necessarily be a benign finding. We believe that ischemic changes are dynamic and can thus evade detection .Our study, however, is replete with limitations. Our study, for instance, was a single-center study, with a relatively small sample size. This means that the results obtained from our study cannot readily be generalised. Further studies with larger sample sizes are therefore required to truly ascertain the predictive ability of ECG. Furthermore, in hospitals experiencing an exorbitant influx of patients, the evaluation of cardiac enzymes might not always be feasible, meaning that a multitude of factors, included the associated costs, ought to be considered.The frequency of NSTEMI was high among ACS patients presenting within 24 hours of the onset of symptoms on a background of unremarkable ECG findings. Unremarkable ECG findings should be construed with caution and thus categorically complemented with the evaluation of cardiac enzymes to conclusively ascertain the absence of an NSTEMI.


Streptomyces, Mycobacterium, Cyanobacteria and Firmicutes revealed that the impact of lifestyle affected the P450 repertoire in these species. However, this phenomenon needs to be understood in other bacterial species. We therefore performed genome data mining, annotation, phylogenetic analysis of P450s and their role in secondary metabolism in the bacterial class Gammaproteobacteria. Genome-wide data mining for P450s in 1261 Gammaproteobacterial species belonging to 161 genera revealed that only 169 species belonging to 41 genera have P450s. A total of 277 P450s found in 169 species grouped into 84 P450 families and 105 P450 subfamilies, where 38 new P450 families were found. Only 18% of P450s were found to be involved in secondary metabolism in Gammaproteobacterial species, as observed in Firmicutes as well. The pathogenic or commensal lifestyle of Gammaproteobacterial species influences them to such an extent that they have the lowest number of P450s compared to other bacterial species, indicating the impact of lifestyle on shaping the P450 repertoire. This study is the first report on comprehensive analysis of P450s in Gammaproteobacteria.The impact of lifestyle on shaping the genome content of an organism is a well-known phenomenon and cytochrome P450 enzymes (CYPs/P450s), heme-thiolate proteins that are ubiquitously present in organisms, are no exception. Recent studies focusing on a few bacterial species such as  Proteobacteria is the most metabolically diverse bacterial phylum [Proteobacteria are divided into seven classes: Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Deltaproteobacteria, Epsilonproteobacteria, Zetaproteobacteria and Acidithiobacillia [Proteobacteria members are well-known human pathogens; especially the class Gammaproteobacteria contain bacterial species that are medically, ecologically and scientifically important [Gammaproteobacteria contain species with different physiology such as anaerobic, microaerophilic, and facultative aerobic, as well as different shapes such as rods, cocci or curve shapes [The phylum l phylum ,2, constl phylum ,2. Basedbacillia . Proteobmportant ,2. Gammae shapes . Most ofhttps://www.ncbi.nlm.nih.gov/genome/microbes/) (accessed on 15 January 2021) and Joint Genome Institute Integrated Microbial Genomes and Microbiomes [Gammaproteobacteria [Because of the pathogenic nature of these species, quite a large number of Gammaproteobacterial species’ genomes have been sequenced and are available for public use at different genome databases, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) , the Natrobiomes . Most ofrobiomes ,29,30. Obacteria ,32. P450bacteria , includibacteria . P450s pbacteria ,36 and bbacteria . P450s’ bacteria . P450s sbacteria ,40,41. Wbacteria  and CYP5bacteria ,43. One bacteria ,45 and ibacteria ,47. Gammbacteria . These mbacteria . The synbacteria . Informabacteria .Streptomyces [Mycobacterium [Cyanobacteria [Firmicutes [Recent studies from our laboratory focusing on bacterial species revealed that the lifestyle of an organism plays a key role in shaping P450 content vis à vis P450s helping microbes to adapt to ecological niches. However, these studies were limited to few groups of bacterial species belonging to the genera ptomyces ,50 and Macterium ,52,53 anbacteria  and Firmrmicutes ,56. FurtGammaproteobacteria genera disclosed that of the 161 genera, species belonging to 41 genera have P450s in their genomes  in their genomes  contributing to 8.3% of total P450s in Gammaproteobacterial species , followeAnalysis of P450 families revealed the presence of 38 new P450 families in Gammaproteobacterial species. Eighty-one P450s were found to be part of these 38 new P450 families. The list of new P450 families found in these species is presented in Firmicutes species  revealed that only a few P450s (18%) are part of these clusters, indicating their involvement in secondary metabolism in Gammaproteobacterial species . The per species . Among 2 species . Among t species . The sam species ,50 and F species , where CMost of the Gammaproteobacterial P450s are orphans without an assigned biological function. Based on homolog P450s from other organisms, some of the Gammaproteobacterial P450s functions can be predicted. CYP51, also known as sterol 14α-demethylase, is a highly conserved P450 across the phyla and stimulates a key enzymatic reaction that involves the stereo-selective three-step oxidative removal of the 14α-methyl group from the sterol during the synthesis of membrane sterol . CYP102 Streptomyces [Mycobacterium [Cyanobacteria [Firmicutes [Firmicutes, it has been reported that the pathogenic/commensal lifestyle was influenced to such an extent that some of the species belonging to quite a large number of genera have no P450s in their genomes [Gammaproteobacteria are commensals with the ability to cause diseases and some of them are strictly pathogenic [Firmicutes [Firmicutes species, and thus the highest P450 diversity percentage compared to other bacterial species  (accessed on 15 January 2021). Based on the percentage identity to the named homolog P450s at the P450 website, selected P450s were assigned to specific P450 families and P450 subfamilies, following the International P450 Nomenclature Committee rule [P450 data mining and annotation were carried out following the standard procedure described previously by our laboratory ,55. Brietee rule ,29,30 thPhylogenetic analysis of P450s was carried out following the protocol previously described by our laboratory ,55. BrieThe generation of the heat map profile was carried out according to the method previously reported by our laboratory ,54,55. GThe identification of secondary metabolite BGCs and P450s forming part of the BGCs was carried out following the procedure previously reported by our laboratory ,55,56. BAll calculations were carried out following the procedure reported previously by our laboratory . The aveFirmicute species [Streptomyces species [P450s and their BGCs data for  species , Strepto species ,50, myco species ,51 and c species  were retGammaproteobacteria. The study revealed that species in this class have few P450s in their genomes compared to other bacterial species. Interestingly, despite having a low number of P450s, Gammaproteobacterial species have the highest P450 diversity percentage compared to Firmicutes species, Streptomyces species, mycobacterial species and cyanobacterial species. The lifestyle of Gammaproteobacterial species, pathogenic or commensalism, had a profound impact on the P450 count in their genomes, leading to having a low number of P450s, as observed in Firmicutes species, indicating that the lifestyle of an organism plays a key role in shaping P450 content. Since P450 enzymes follow typical nomenclature criteria, this study will serve as a reference for P450 annotation in the class Gammaproteobacteria. Studies are in progress to determine the impact of lifestyle in other bacterial species.Sustained research into genome sequencing resulted in the sequencing of a large number of species genomes. This gives us an opportunity to look at specific protein families, their distribution and their role in organisms’ physiology. This study is an example of such work, where cytochrome P450 monooxygenases (CYPs/P450s) were analyzed in the bacterial class Another interesting result from the study is the identification of secondary metabolite biosynthetic gene clusters that have P450s in the Gammaproteobacterial species. In this post-genomic era, before entering the laboratory one can analyze large numbers of genomes and select the best candidates for the production of unique/novel secondary metabolites. This approach is now gaining momentum, as it saves a tremendous amount of wet-laboratory work, money and laborious practice. In this study, we followed this approach and identified secondary metabolite biosynthetic gene clusters that seem to be novel, as they have the lowest percentage identity to known or characterized secondary metabolite gene clusters, indicating that the compounds synthesized by these gene clusters are novel and possibly have biotechnological values. Future studies include cloning of these novel clusters to enable the synthesis of metabolites and assessment of the biological properties of the synthesized metabolite.


Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe.Target of rapamycin (TOR) is a serine/threonine kinase that modulates cell growth and metabolism in response to environmental changes. Transfer RNA (tRNA) is an abundant and ubiquitous small non-coding RNA that is essential in the translation of mRNAs. Beyond its canonical role, it has been revealed that tRNAs have more diverse functions. TOR complex 1 (TORC1), which is one of the two TOR complexes, regulates tRNA synthesis by controlling RNA polymerase III. In addition to tRNA synthesis regulation, recent studies have revealed hidden connections between TORC1 and tRNA, which are both essential players in eukaryotic cellular activities. Here, we review the accumulating findings on the regulatory links between TORC1 and tRNA—particularly those links in the budding yeast  Target of rapamycin (TOR) is a highly conserved serine/threonine kinase among eukaryotes. TOR acts as a master regulator that controls cell growth and metabolism in response to environmental changes . TOR Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe, both of which have contributed to our understanding of the cellular signaling pathway, including TOR signaling.Transfer RNAs (tRNAs) are abundant, small, and ubiquitous non-coding RNAs that are essential for decoding genetic information, achieving this by delivering amino acids to the growing polypeptide chains in ribosomes. Each tRNA is charged with a cognate amino acid on its 3′ end, in an ATP-dependent manner. This process is catalyzed by aminoacyl-tRNA synthetases (ARSs). Each aminoacyl-tRNA is brought to the A-site of the ribosome by the translation elongation factor 1A; then, its anticodon forms base pairs with a corresponding mRNA codon. In recent years, a growing number of reports have shown that tRNAs have a wide range of cellular functions beyond their canonical role as adaptor molecules during protein synthesis ,5. For eS. cerevisiae, and the counterparts of RagA/RagB and RagC/RagD are Gtr1 and Gtr2, respectively [S. cerevisiae genes that encode the Rag proteins, namely GTR1 and GTR2, are nonessential; this suggests that the Rag system is not the sole pathway to amino acid sensing by TORC1. In a study wherein the alternative mechanisms underlying the TORC1 regulation were elucidated, the tRNA-mediated inactivation of TORC1 in S. cerevisiae was demonstrated [In mammalian cells, the Rag complex, which is composed of RagA/RagB and RagC/RagD, regulates TORC1 by responding to amino acid levels. Rag proteins are conserved in ectively ,9,10. Thnstrated .tRNA severely inhibits in vitro TORC1 activity. The inactivation of RNA polymerase III, which transcribes tRNA, results in the maintenance of TORC1 activity upon starvation. From these observations, it has been proposed that amino acid starvation increases the number of free uncharged tRNA that is liberated from protein synthesis; then, the accumulated free tRNA inactivates TORC1 . The resS. pombe, TORC1 represses sexual differentiation and exerts an essential function for vegetative growth, via a nutrient-sensing pathway [tor2, which encodes the catalytic subunit of S. pombe TORC1, induce an ectopic initiation of sexual differentiation, even under nutrient-rich conditions at non-permissive temperatures. Several mutants that phenocopy the TORC1 mutant have been isolated. Intriguingly, many of their responsible genes encode factors that are involved in tRNA expression and modification, such as ARSs, a subunit of RNA polymerase III, or a tRNA specific adenosine deaminase subunit [S. pombe. The observation that the overexpression of tRNA precursors prevents TORC1 downregulation upon nutrient starvation and inhibits sexual differentiation indicates that tRNA precursors regulate TORC1 activity in a positive manner  [Rapamycin has no impact on the cell growth of apamycin ,23,24. Iapamycin   .tRNA modifications occur most frequently within the anticodon loop, especially at positions 34 and 37. Position 34, which is the so-called wobble position, pairs with a third mRNA codon base, in the A-site of the ribosome, during translation . A uridiS. pombe, mutant strains that lack the Elongator complex subunits show exaggerated sensitivity to rapamycin [tor2, which encodes the catalytic subunit of S. pombe TORC1, increases the sensitivity to rapamycin. This observation suggests that the hyperactivation of TORC1 leads to a higher sensitivity to rapamycin, as in the case of the downregulation of TORC1 activity, even though there may be a possibility that the overexpression of the catalytic subunits results in the downregulation of TORC1 by perturbing the balance of the components. From these observations, it is concluded that Elongator contributes to the downregulation of TORC1 by promoting the expression of repressors, such as Tsc1 and Tsc2. The positive regulation of TORC2 by Elongator has also been proposed [Interestingly, in apamycin ,38 . Shown in Rapamycin cannot prevent the growth of S. pombe . HoweverS. pombe ,45, inhie growth . Gene exe growth . We survS. cerevisiae, intron-containing pre-tRNAs are exported from the nucleus to the cytoplasm for the removal of introns, and the spliced tRNAs return to the nucleus via a so-called tRNA retrograde transport [As mentioned above, tRNAs are synthesized as tRNA precursors (pre-tRNAs) prior to the post-transcriptional modifications. In ransport ,49. Aminransport ; this suransport ,51; howeransport . This suS. pombe mutant cells that were lacking Los1, which is a nuclear tRNA export receptor, showed sensitivity to rapamycin [S. pombe.Notably, the apamycin   have been reported to act as a cytoplasmic leucine sensor to activate TORC1, although the detailed mechanisms on how LeuRS acts in the TORC1 pathway are different between these two species [In addition to Sestrin 2,  species ,78,79,80S. cerevisiae, LeuRS Cdc60 was isolated as a coprecipitating protein with Gtr1 [In ith Gtr1 . LeuRS hS. pombe and S. cerevisiae. We isolated S. pombe ARS mutants, including a LeuRS mutant, during a screening for mutants that phenocopy the TORC1 mutant [S. cerevisiae, in addition to LeuRS, other ARSs, such as HisRS and IleRS, are involved in TORC1 regulation [S. cerevisiae ARS mutant cells, and that the accumulated free tRNAs inhibit TORC1 activity. Further studies would reveal similarities and differences in the mode of action of tRNAs in the TORC1 pathway of the two yeast species.While the novel function of LeuRS as a leucine sensor is appealing, we have proposed alternative rational models for both 1 mutant . In the gulation . TherefoThere is accumulating evidence that tRNAs have more diverse functions than originally thought. For instance, tRNA-derived small RNAs (tsRNAs) have drawn increasing attention; tsRNAs are produced by cleavage at specific sites in tRNAs or pre-tRNAs, and have various biological functions, including the regulation of gene expression, inhibition of translation, prevention of apoptosis, and regulation of epigenetic inheritance. Furthermore, tsRNAs have been demonstrated to be involved in tumorigenesis ,6,7,81. S. cerevisiae and S. pombe. tRNA is likely to be one of the ideal molecules that transduces nutrient availability, especially for amino acid availability, to TORC1, as it enables the fine-tuned regulation of the TORC1 activity in response to the cellular levels of each of the 20 amino acids. Although, as discussed above, the modes of action of tRNA seem to be different between S. cerevisiae and S. pombe, it is intriguing that tRNA plays important roles in these two evolutionarily distant yeast species. A comparison between the two divergent systems will bring us closer to understanding the novel regulatory mechanisms of TORC1 signaling. Further investigations, not only in yeasts but also in other eukaryotes, would lead us to a comprehensive understanding of the promising tRNA roles in the TORC1 pathway.It has long been known that TORC1 regulates tRNA synthesis in downstream events. As we have discussed here, studies over the past few years have uncovered the involvement of tRNA in the regulation of TORC1 activity, in both


Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017.Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the six models.The LightGBM model exhibited the highest AUC , which was significantly higher than that of LR , SVM  and ANN , but did not differ significantly from that of RF  and XGBoost . In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%), precision (0.695), and F1 score (0.725). Feature importance identification indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32 and 43%, respectively.This study suggests that ensemble learning models, especially the LightGBM model, can be used to effectively predict the peak events of CVDs admissions, and therefore could be a very useful decision-making tool for medical resource management. Another national time-series study conducted in 184 Chinese cities linked temperature variability to the increase of hospital admissions for CVDs and its subtypes using over-dispersed Poisson regression models [Cardiovascular diseases (CVDs) are the leading cause of death worldwide; about 17.9 million deaths were attributable to CVDs in 2016, representing approximately 31% of all global deaths in that year , such asn models . Althougn models –17, theyWith an increasing number of CVDs patients putting pressure on the limited medical resources, the prediction of healthcare demands, particularly those associated with peak events, has gained greater attention. Time series forecasting approaches, such as the autoregressive integrated moving average (ARIMA) model and the seasonal ARIMA model, are widely applied in predicting problems regarding emergency department visits , 19, newRecently, machine learning algorithms, which can solve the nonlinear relationship among multi-dimensional variables, have been shown to be effective in prediction, and are being used successfully in various healthcare applications, such as medical diagnosis , 23 and In this study, we contribute to the existing body of knowledge by developing and comparing various machine learning models in predicting the peak demand days of CVDs admissions based on hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017. Six types of machine learning models, including logistic regression (LR), support vector machine (SVM), ANN, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were constructed, and their predictive performances were also compared. The study shows the potential of machine learning approaches for predicting peak events of CVDs admissions, and identifies the most sui model for decision making.This study attempted to predict the peak demand days of CVDs admissions using machine learning techniques. The block diagram of the classified prediction process is shown in Fig. The details are discussed in the following sub-sections.Data for the daily number of hospital admissions for patients with CVDs who lived in urban areas of Chengdu was obtained from the Health Information Center of Sichuan Province, China. This data contains aggregate numbers of CVDs admissions in all the tertiary and secondary hospitals of Chengdu each day with primary diagnosis of CVDs  from 1 January 2015 to 31 December 2017, which is 1096 days of continuous data.Additionally, we focused on the peak demand of CVDs admissions, and the binary variable was generated from the daily number of CVDs admissions. In the absence of a known threshold for daily CVDs admissions, the peak demand was defined on the basis of an 85th percentile threshold  by reference to the previous studies , 33. Spehttp://data.cma.cn/).Meteorological data, including temperature, relative humidity and rainfall, were derived from the Chengdu Meteorological Monitoring Database (2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm), PM10 (particulate matter with aerodynamic diameter ≤ 10 μm), SO2, NO2, CO and O3, were obtained from the China National Environmental Monitoring Center (http://www.cnemc.cn/), which provides real-time monitoring of hourly concentrations of air pollutants to the general public. We averaged the 24-h mean concentrations for PM2.5, PM10, SO2, NO2 and CO, and calculated maximum 8-h moving average concentrations for O3 from the air quality monitoring stations interspersed among the urban areas of Chengdu. Concentrations of particulate matter with an aerodynamic diameter between 2.5 and 10 μm (PMC) were calculated by subtracting daily average concentrations of PM2.5 from PM10 [Hourly data of air pollutants, including PMrom PM10 , 34.Data for the daily number of hospital admissions for CVDs, meteorological data and air quality data were collected from different data sources. We merged these three datasets to form a time series dataset by date (i.e. 1 January 2015 to 31 December 2017). The time series features were extracted from date, including year, month (month of year), day (day of month), holiday (public holidays) and DOW (day of week).During the study period, the percentages of missing values from the monitoring stations were 1.28% (14/1096) for meteorological conditions, and 3.19% 35/1096) for air pollutants. The linear interpolation which has acceptable performance and reliability was used to fill in the missing values of meteorological conditions and air pollutants /1096 for.As illustrated in the above section, the features for predicting the peak demand days of CVDs admissions included time series features, meteorological condition features and air pollutant features. Accumulating epidemiological studies have suggested that the effect of meteorological conditions and air pollutants on CVDs admissions is delayed, and the lag effect is related to the regional environment , 12, 37.2.5, PM10, PMC, SO2, NO2, CO and O3 were associated with CVDs admissions, with the minimum GCV values at lag04, lag06, lag06, lag3, lag3, lag3, lag0, lag0, lag0 and lag6, respectively.The results demonstrated that temperature, relative humidity, rainfall, PMFinally, the independent variables for forecasting the peak demand days of CVDs admissions included fifteen features, which are shown in Table In this study, six well-accepted machine learning algorithms, including LR, SVM, ANN, RF, XGBoost and LightGBM, were applied to develop predictive models with the unique feature set. These machine learning methods were considered according to their following characteristics.LR is a common and basic algorithm, which is widely used in disease risk prediction and epidemiology . SVM is All above-mentioned models were trained and tested on a partitioned 80/20 percentage split of the dataset by stratified random sampling. Simultaneously, in situations where there was imbalanced class data combined with unequal error costs, these models’ performance metrics were not representative of reasonable performances. Therefore, it was necessary to balance the dataset to get true performance values for the classifier; hence, we adjusted weights inversely proportional to class frequencies in the input data when training the machine learning models.The parameters of these six predictive models were determined by grid search and 10-fold cross-validation in training the dataset. To be specific, we partitioned the training dataset into ten equally sized pieces, and we utilized the grid search with nine pieces to tune the parameters, while the remaining piece was used as the validation set. We repeated this process ten times. The best parameters for predictive models were obtained with the best score, which itself was obtained by averaging the process of repetition mentioned in the previous sentence. Table p-value < 0.05 was deemed to indicate statistical significance) [We calculated the AUC from receiver operating characteristic (ROC) analysis to evaluate the predictive utilities of the models, and the AUC of the six machine learning models was compared based on the DeLong method (ficance) . Meanwhi3 for PM2.5, 99.3 μg/m3 for PM10, 39.0 μg/m3 for PMC, 13.9 μg/m3 for SO2, 55.0 μg/m3 for NO2, 96.0 μg/m3 for O3 and 1.1 mg/m3 for CO.The statistical information of daily CVDs hospital admissions, meteorological conditions and air pollutants concentrations is summarized in Table p-value < 0.001), SVM  and ANN , but did not differ significantly from RF  and LightGBM .Based on the above-mentioned features in Table p-value < 0.001), SVM , ANN , but did not differ significantly from RF  and XGBoost .Based on the validation result for the training dataset, we predicted the peak demand days for CVDs admissions in an independent testing dataset. The ROC curve for the predictive models in that testing dataset is shown in Fig. Furthermore, we used log-loss, accuracy, sensitivity, specificity, precision, and F1 score to compare the performances of these six machine learning models in the independent testing dataset Table . The LigAs illustrated in the above section, the LightGBM model achieved the best performance; it offers the most powerful predictors for predicting the peak demand days of CVDs admissions. The identification of feature importance based on LightGBM is shown in Fig. The six machine learning models were developed to predict the peak demand days for CVDs admissions, and as a result of our study, the optimal model has been identified. To the best of our knowledge, no studies have applied machine learning models other than ANN in the prediction of peak event of healthcare demand. This is the first study to construct and compare various machine learning models in terms of predicting the peak events of CVDs admissions using meteorological data, air quality data and hospital admissions data.Our study found that the ensemble learning models, including LightGBM, RF and XGBoost, outperformed ANN, SVM and LR, achieved overall accuracies of > 0.86 and AUCs of > 0.92. This suggests that the ensemble learning models have better generalization capabilities compared to other models for predicting the peak demand days of CVDs admissions. The LightGBM exhibited the best performance among the ensemble learning models. Compared with ANN, SVM and LR, the AUC of LightGBM significantly improved by 5.65, 12.66 and 11.61%, respectively. Even though most predictive models have higher recall and lower precision, this could be acceptable as insufficient allocation of medical resources in peak days can lead to costly outcomes. The results of our study indicate that ensemble learning models are well suited for the prediction of peak demand for healthcare services.The lag patterns of meteorological conditions and air pollutants have been well-documented in epidemiological studies , 12, 16,Environmental exposure, such as ambient air pollution and extreme temperatures, is an important but underappreciated risk factor contributing to the development and severity of CVDs . AccumulOur study has several strengths. First, considering the lag effects of the complex mixture of environmental exposure and their regional differences, we utilized an over-dispersed GAM to analyze the lag effects of meteorological conditions and air pollutants on CVDs admissions, and chose the lag day with the minimum GCV value as the optimal predictor, rather than using the current day or relying on previous research, which makes our predictive models more practical. In addition, we applied six well-accepted machine learning algorithms to construct predictive models, which indicate our commitment to present a wide variety of approaches. Specially, LR represents the basic machine learning model, SVM and ANN are widely used in prediction, and RF, XGBoost and LightGBM are ensemble learning models. As discussed earlier, we found that ensemble learning models, especially the LightGBM model, have higher prediction capabilities than LR or ANN, which can benefit decision makers in finding more suitable models for the prediction of healthcare demand, especially during peak events. To the best of our knowledge, this study is the first to develop and compare various well-accepted machine learning models to predict the peak events of CVDs admissions that consider environmental exposure. Our results contribute to the limited research in this filed, as they provide useful and comprehensive information to those who seek to identify the most suitable model for decision making.Our study also has some limitations that need to be addressed. First, we considered only two well-studied environmental exposures: meteorological conditions and ambient air pollutants, but some other environmental factors, such as exposure to the metals arsenic, cadmium and lead, also play important roles in the development and severity of CVDs . Second,This study used machine learning approaches to forecast the peak demand days for CVDs admissions based on hospital admissions data, air quality data and meteorological data. The results revealed that ensemble learning models, especially the LightGBM model, can accurately predict the peak events of CVDs admissions. Meanwhile, the identification of feature importance based on LightGBM indicated that meteorological conditions and air pollutants made significant contributions to the accuracy of prediction. These findings show that machine learning approaches have potential in the prediction of the peak events of CVDs, and the predictive capacity of ensemble learning models makes them valid tools supporting decisions regarding medical resource management.


The world has reached substantial progress in reducing child mortality in recent decades . Every yGlobally, the under-five mortality rate dropped from 93  in 1990 to 41 (39–44) deaths per 1,000 live births in 2016, with the 3.2% annual reduction rate. Among countries in this region, United Arab Emirate, Lebanon, Kuwait, Bahrain and Qatar with the 8, 8, 8, 8 and 9 under-five mortality rate respectively per 1000 live births has the better situation and the worst situation belongs to Yemen with the rate of 55 (40–76) . While tDisparities in child survival exist across countries in Eastern Mediterranean countries and the countries involved in the war, such as Iraq and Yemen, are not in a good position. Although Iran reached the goals of Millennium Development Goals (MDGs) and the substantial decline occurred for under-five mortality rate, however, compared to some countries in this region require more effort is to further improve these indicators.


Camellia kissii is cultivated for a long time as an oil crop for edible and industrial oils, and has the functions of high oil production rate and unique health care. The complete chloroplast (cp) genome sequence of C. kissii is 156,961 bp in length with GC content of 39.29%. It presents a quadrate structure, including a large single-copy (LSC) region , a small single-copy (SSC) region , and a pair of inverted repeats (IRs) . Meanwhile, 15 complete chloroplast genome of Camellia was aligned to explore the phylogenetic significance of Camellia. And the genetic relationship between Camellia kissii and Camellia huana was found to be closest. Camellia kissii, which grows in the evergreen broad-leaved forest, is a kind of crop with important economic value. At present, C. kissii is cultivated for a long time as an oil crop for edible and industrial oils, and has the functions of high oil production rate and unique health care . Illumina Hiseq platform, 2 × 150 two-terminal sequencing strategy was used to sequence the library. The complete chloroplast genome was assembled using a comparative mapping method  was selected as the initial reference genome. The annotated chloroplast genome of Camellia kissii has been submitted to GenBank (NCBI Accession Number MN635793). The voucher specimen (CD_02) was reserved in State Key Laboratory of Tree Genetics and Breeding, Research Institute of Subtropical Forestry, Chinese Academy of Forestry.The samples of Camellia japonica through the Bowtie2 software  region , a small single-copy (SSC) region , and a pair of inverted repeats (IRs) . In addition, the GC content of LSC and SSC regions (35.29% and 30.53%) is lower than that of IR regions (42.02%). The cp genome was annotated with 129 functional genes, consisting of 81 protein-coding genes, 44 transporter RNAs, and 4 ribosomal RNAs.During the process of genome assembly, we have carried out strict control and quality evaluation of sequence data. After filtering 712,687 raw reads , 28,023,651 clean reads  were aligned to Camellia, the entire genome of 15 chloroplasts of Camellia was aligned using MEGA v7.0.14  was found to be closest.In order to explore the phylogenetic evolution of


Background: lactic acid fermentation was recently proposed to produce fruit and vegetable beverages with high nutritional value. In this study, a wide screening of strains and fermentation parameters was carried out to develop fermented tomato-based drinks containing viable cells and potentially bioactive metabolites. Methods: six different products  were used as substrate for fermentation. After preliminary testing, eight fermentation conditions for each tested product were selected. The final products were stabilized with pasteurization or refrigeration and further characterized in terms of (i) antioxidant activity and (ii) total polyphenols. Results: selected strains were able to grow in almost all tomato-based products except for one extract. Antioxidant activity and total phenolic content depend on products and fermentation conditions used and, except for tomato puree, an overall increase was observed. The best nutritional profile was reached in fermented samples stored at refrigerated temperature without thermal stabilization. Conclusion: an integrated data vision allowed to choose, for each substrate, the best combination of strains to produce novel fermented tomato-based products with different application perspectives. Fruits and vegetables are essential sources of water-soluble vitamins (vitamin C and group B), phytosterols, fiber and minerals. Numerous studies have highlighted that their consumption over time help to prevent chronic diseases such as hypertension, vascular and cardiac diseases ,2,3.In recent years, lactic acid fermentation of vegetable matrices as a possible strategy for producing beverages that satisfy the needs of the consumers, who increasingly look for ready-to-drink products characterized by high nutritional properties, with health value gaining more attention . TherefoFruits and vegetables, due to their low pH, high concentration in sugars and the presence of polyphenols, represent an unfavourable growth environment for starter microorganisms ,10. To aTomato is an excellent source of health-promoting compounds due to the balanced content of minerals and antioxidants including vitamins C and E, lycopene, β-carotene, lutein and flavonoids such as quercetin. Studies have shown correlations between tomato consumption and lower risk of certain types of cancer, cardiovascular diseases and age-related macular degeneration ,14,15,16The present study aimed to find the best processing parameters in order to develop a fermented tomato-based drink with good nutritional characteristics. A screening of different LAB strains, considered as single or co-culture, originating from different sources, was evaluated to assess whether, through lactic acid fermentation, it is possible to obtain a new type of drink by analyzing (i) bacterial growth, (ii) antioxidant activity and (iii) content of polyphenols.Tomato juices were provided by Mutti, a tomato industrial company, located in Traversetolo . Six commercial products belonging to different categories were considered for this study: three fruit and vegetable extracts, two tomato juices and one tomato puree.The main ingredients of fruit and vegetable extracts are the following: tomato, orange, carrot and ginger for the first extract (CA); yellow tomato, apple, carrot and lemon for the second one (CG); tomato, strawberry, pomegranate and violet carrot (CV) for the third . Tomato Pediococcus acidilactici , Lacticaseibacillus casei , and Lacticaseibacillus rhamnosus . All the strains belong to the collection of the Department of Food and Drug of the University of Parma. The bacterial stock cultures were maintained as frozen at −80 °C in de Man Rogosa and Sharpe (MRS) broth  added with 12.5% glycerol (v/v).Three bacterial strains isolated from dairy products, belonging to different LAB species, were used for fermentation: °C for tomato exctract, while tomato puree and tomato juices were treated for 10 min at 100 °C; refrigeration was set up at 4 °C.In order to select the optimal fermentation conditions for each of the six substrates considered, a preliminary screening based on 179 trials was carried out . These t0) and at the established times of fermentation. Ten-fold dilutions in Ringer solution  were plated in MRS agar (Oxoid), followed by incubation for 48 h in aerobic condition at 37 °C. The pH of all samples was also measured by pH meter  in duplicate at the beginning and at the end of fermentation.A first sorting was made on the basis of the results of increase of viable cells after fermentation. Bacterial concentration was determined after inoculum , washed twice with Ringer’s solution , and re-suspended in sterile distilled water to reach the final concentration of 9.0 Log CFU/mL. Co-cultures were obtained by mixing single revitalized strains in equal volume and further diluting the mixture to reach the desired concentration in the product. Each culture/co-culture was inoculated into CA, CG, CV, SPC, SPP juices and C puree in different concentrations ranging between 2 Log CFU/mL and 5 Log CFU/mL. Samples were incubated at 37 °C for 6, 14, 24, 30 and 48 h. The initial inoculum and the growth ability after fermentation were checked by plate count agar on (Oxoid) at 37 °C for 48 h.Before fermentation, strains were revitalized according to Ricci et al. . The staThe antioxidant activity was evaluated by the DPPH radical scavenging activity assay . This spTotal phenolic content (TPC) of all samples were analysed by the Folin–Ciocâlteu’s method according to Singleton et al. . The absp < 0.05) between fermented samples and the control.Analyses were carried out in triplicate. The data are presented as the mean of three replicate ± standard deviation. Independent sample T-test was used to determine significance differences , followed by CA, CG and SPC where the increase was closed to 4 Log CFU/mL. Finally, SPP allowed an increase of about 3 Log CFU/mL.P. acidilactici 3992 and 24 h of incubation, reached the highest microbial increase. Otherwise, in the same substrate, the combination no. 4, with inoculum size of 3 Log CFU/mL of L. rhamnosus 1473 incubated for 14 h, led to a growth of only 1 Log CFU/mL.Overall, a heterogeneous growth trend for different combinations was observed even in the same substrate, as highlighted in P. acidilactici 3992 and L. rhamnosus 1473, fermented for 48 h, reached the highest bacterial increase (4 Log CFU/mL). The fermentation with monoculture of L. rhamnosus, conducted for 24 h with the same inoculum size (combination no. 6), showed a minimum increase (ca. 1 Log CFU/mL).The combination no. 8 of CG, co-culture of The combinations no. 8 and no. 3, even with a slightly different trend, turned out to be the best and the worst, respectively, in supporting the microbial growth .P. acidilactici 3992 was the strains with highest growth ability, of about 6 Log CFU/mL  reduction in pH were noted in all the samples.Despite the microbial growth observed, no significant  and total polyphenols content (Folin–Ciocâlteu) were determined . Figure Differences were observed among samples. The increase of the antioxidant activity was observed in CG extracts but particularly in tomato juices (SPC and SPP). Overall, polyphenols generally increased after fermentation in the same samples and in some CA-fermented products, especially when stabilized with refrigeration. This tendency in increasing polyphenols content and antioxidant activity in fermented tomato juices is in agreement with the data reported by Liu et al. .The different performance of the combinations employed allowed to select the best parameters to be used in each product, which are easily identified in the scatter plot .In general, in CA, differences in the polyphenol content were observed due to the stabilization treatment (NTT or TT) after fermentation in samples no. 1, 2, 5 and 6 termically treated where there was a decrease . The besAmong the fermented CG products, sample no. 3 seems to be the best combination with higher polyphenol content and antioxidant activity.L. rhamnosus, used in combination no. 3 and no. 6, showed a different behavior, in term of polyphenol content , where a considerable microbial increase was noted, showed a lower value of antioxidant activity and polyphenols content, in comparison to the control. To note, after the application of thermal treatment, we observed an increase of polyphenols and antioxidant activity (combination no. 2 and no. 4), as already seen in some CG and CG samples . AlthougAnalysis of the antioxidant activity revealed a slight increase, in comparison to the control, in almost all refrigerated and in some pasteurized samples . The modConsidering the results obtained, some substrates, such as orange extract (CA), yellow extract (CG) and tomato juice with “pizzutello” variety (SPC), demonstrated more suitabillity for bacterial growth thus for refrigerated drink preparation. In particular, the use of combination no. 5 for orange extract and combination no. 7 for pizzutello tomato juice allowed a product rich in viable cells and metabolites to be obtained. By contrast, in order to develop a pasteurized tomato drink, combination no. 8 in the pizzutello tomato juice sample can lead to pasteurized products with improved properties.This study explored the use of a wide set of LAB strains of dairy origin and fermentation conditions aimed to develop a fermented tomato-based drink with good nutritional characteristics.As expected, the nutritional features of tomato products, such as antioxidant activity and polyphenol content, firstly depend on the composition substrates. In this context, violet extract (CV) and tomato puree (C) appear to be the most interesting, especially for polyphenol concentration. Starting from these observations, we have monitored the contribution, in function of the combinations used, of fermentation and the effect of refrigeration or pasteurization. A significant microbial growth was recorded in all samples, except for violet extract, but it was not always related to the improvement of antioxidant activity and polyphenol content.This is the case of tomato puree, which appears unuitable for lactic acid fermentation. For the other tomato-based substrates, at least one combination was able to increase nutritional profile. Despite the requirement for future studies on this topic, which should aim to carry out the management of an effective technology transfer and to evaluate the products during storage, the results of this study highlight the possibility of formulating new beverages fermented with LAB of dairy origin characterized by high antioxidant activity and polyphenolic content.


Staphylococcus aureus (LA-MRSA) is a major concern in many parts of the world, including Pakistan. The aim of this study was to investigate the prevalence of MRSA in slaughterhouses and meat shops in Rawalpindi-Islamabad, Pakistan, 2018–2019. A total of 300 samples were collected: 40 from each of working area, tools , butcher hands and beef, 30 from each of chicken and mutton, 20 from each of nasal and rectal swabs. S. aureus was phenotypically identified by performing gram staining and biochemical tests. 150 of the 300 samples were confirmed to be S. aureus by phenotypic identification. MRSA was identified among S. aureus positive isolates by performing disk diffusion test and by detecting S. aureus-specific genes such as 16s rRNA, nuc, mecA, spa, and coa. Out of 150 isolates 96 (63%) showed resistance to antibiotic cefoxitin, known as a potential marker for detecting MRSA. While all 150 isolates have shown complete resistance to the four antibiotics neomycin, methicillin, ciprofloxacin and tetracycline. The nuc and 16s rRNA genes were detected in all 150 S. aureus-positive isolates and 118 (79%) were confirmed to be MRSA through the detection of the mecA gene. MRSA prevalence was highest in chicken  followed by beef , mutton , knives , nasal swabs , working area , rectal swabs , hooks , and butcher hands . 50 MRSA-positive isolates were chosen to identify two virulence factors (spa and coa gene). Of the 50 MRSA isolates subject to coa and spa gene typing, 27 (54%) were positive for the coa gene and 18 (36%) were positive for the spa gene, respectively. To the best of our knowledge, this was the first study on the molecular identification of MRSA in meat samples from Pakistan. High prevalence of MRSA in meat samples demand for implementation of proper hygienic practices and procedures during the slaughtering, transport and marketing of meat and meat products in order to prevent the spread of these bacteria to the human population.Livestock-associated methicillin-resistant  Staphylococcus aureus is a Gram-positive, coagulase- positive pathogen belong to the family Staphylococcaceae. This is a spherical bacterium about 1 μm in diameter that forms grape-clusters (Staphylococcus aureus (S. aureus) is one of the most common microorganisms colonizing the nasal cavity of humans and different animal species , and scalded skin syndrome  strain was first identified in United Kingdom in 1961, continues to be a serious hospital concern for public health  398, was first identified from pigs in The Netherlands and France in food producing animals in Europe, particularly in pig herds, turkeys, veal calves and broiler flocks . The termecA gene, which resides on the lactams of a mobile genetic element called the staphylococcal chromosome cassette (SCCmec) (spa) and staphylococcal coa (coagulase) gene  that has a weak affinity to all β-lactams. The protein is encoded by the (SCCmec) . To classe) gene . Virulennfection . Both viThe widespread use of antimicrobials in animal production is believed to promote the emergence and spread of MRSA because of selection pressure induced by antimicrobials . OvercroPakistan, as a developing country, suffers primarily from antibiotic resistance, which is a concern not only for Pakistan, but for the entire human/animal population . Weak stS. aureus phenotypically and molecularly through amplification of 16S ribosomal RNA, mecA, nuc, spa, and coa genes from selected slaughter houses and meat shops in twin cities Rawalpindi, Islamabad, Pakistan during 2018–2019.Detection of staphylococci in meat is often related to poor hygienic practices during processing, shipping, slicing, storage and point of sale by individuals involved in the production process. Therefore, the main objective of this study was to identify The ethical approval of the study was taken from ethical review committee of Sarhad University, Peshawar and Pakistan Institute of Medical Sciences (PIMS), Islamabad, Pakistan. The institutional committees approved the experiments carried out for the current research described in the “Materials and Methods” section of the manuscript. All procedures have been carried out in compliance with the relevant regulations and standards.–1. The dilutions 10–3, 10–4, and 10–5 were used for inoculation. The swab samples were placed in peptone water-containing falcon tubes and then centrifuged at 5,000 rpm until all material on swab is dissolved fully in peptone water.Three hundred samples from various slaughterhouses and meat stores in different areas of Rawalpindi, Islamabad, Pakistan were collected during 2018–2019. The sample size for this study was calculated using prevalence formula in a software N-Query Advisory  . A totalS. aureus using cotton swabs. A sample of 100 μl was transferred to the Mannitol salt agar  medium and distributed uniformly. The plates were incubated at 37°C overnight. Isolated colonies that showed fermentation on MSA medium were subcultured again on the MSA in order to get pure cultures. Bacterial isolates were cultured on blood agar  medium containing 5% heparin free sheep blood added to the blood agar base after autoclaving and cooling to 50°C. S. aureus have the ability to hemolyse sheep blood and shows alpha (α) hemolysis observed in the form of clear zones around colonies. Until observations, plates were incubated at 37°C for 24–48 h. Staphylococci have been detected phenotypically using standard methods specific to the detection of enzymes and certain biochemical processes. These techniques include culturing, gram staining, catalase test, coagulase and DNase test  for the growth of ase test . FollowiTo confirm the antibiotic susceptibility of bacterial isolates to specific antibiotics, Kirby Bauer or disk diffusion test was used . MuellerS. aureus isolates was performed by amplification of the Staphylococci 16s rRNA gene, the species-specific nuc gene and the mecA-resistant gene. The genomic DNA was extracted using the DNA extraction method CTAB (Cetyltrimethylammonium bromide) . The PCR conditions set for nuc gene amplification reaction were, an initial denaturation 95°C for 5 min, 34 cycles of amplification . For product visualization, 8 μl of DNA sample was mixed with 2 μl of 6× loading dye. Then 1.5% of gel was prepared in 1× TAE buffer and 100-bp DNA ladder  with a voltage of 100V for 20–25 min was used to validate the amplified product. After the gel electrophoresis was completed, the gel results were visualized using the Gel doc system .A molecular analysis of phenotypically identified bromide) . Before bromide) . The PCRcoa (coagulase) and x region of Staphylococcus protein A (spa) genes is carried out for 50 selected MRSA isolates based on the identification of mecA (resistant) gene. DNA extraction was performed using the QIAamp DNA Mini Kit , with manufacturer instructions. Gene-specific primers were used as described . The PCR cycling conditions used for spa gene were: 34 cycles of amplification . For product visualization, 8 μl of DNA sample was mixed with 2 μl of 6× loading dye. Then 1.2% of gel was prepared in 1× TAE buffer having ethidium bromide (0.5 μg/ml) and 100-bp DNA ladder  was used to validate the amplified product. The voltage used was 80 V for 1 h. After the gel electrophoresis was completed, the results of the gel were visualized using the Gel doc system .The amplification of escribed . The seqS. aureus positive samples were subcultured further on MSA and selected for other biochemical tests. All 150 samples were grown in the blood agar, and all showed beta-hemolysis activity  medium. Of the 300 samples, 150 fermented mannitol and showed yellow colonies on MSA . In ordeactivity . Gram stactivity . Further. aureus –S6.S. aureus differed among sample sources and specimen types based on the phenotypic findings. Out of a total of 300 samples, 150 samples were positive for S. aureus with an overall prevalence of 50%. The highest prevalence of S. aureus was observed for chicken  followed by beef , knives , mutton , nasal swabs , and working area . The least prevalence of S. aureus was found in rectal swabs , hooks , and butcher hands , respectively  gene (mec A) was identified in  of the samples. The mecA gene primer gave the PCR product equal to 533 bp  were produced (coa gene yielded a single amplicon in each isolate. Two amplicons of different sizes (681 and 891 bp) were developed , hooks , working Area , butcher hands , nasal swabs , rectal swabs , chicken , mutton , and beef . The distribution of coa gene in different sample types is as follows: knives , hooks , working Area , butcher hands , nasal swabs , rectal swabs , chicken , mutton , and beef .Of the 50 MRSA isolates subject to produced . One hunproduced . PCR ampeveloped . Six huneveloped . The disThe emergence and persistent spread of drug-resistant bacteria has become one of the most daunting problems facing the world today . Global S. aureus, with an overall prevalence of 50%. For confirmation of MRSA all 150 isolates were subjected to a disk diffusion test and further molecular analysis. Ninety-five out of one hundred fifty S. aureus isolates were considered MRSA by antimicrobial resistance pattern based on the frequency of resistance to cefoxitin (30 μg) which is known as a potential marker for detecting methicillin resistance  were conducted via PCR. This technique has been used previously for the detection of S. aureus and methicillin resistant S. aureus  and is used for the detection of MRSA and for two virulence factors (spa gene and coa gene) of highly pathogenic S. aureus. All 150 isolates were detected positive for nuc and16S rRNA genes. Of the 150 Staphylococci phenotypically identified in our study only 118 (63%) S. aureus isolates were considered MRSA by molecular detection of mecA gene.The molecular confirmation of . aureus . In thismecA gene is highly conserved among staphylococcal strains and is being used as a potential biomarker for the detection of methicillin resistance S. aureus (MRSA)  followed by beef , mutton , knives , nasal swabs , working area , rectal swabs , hooks , and butcher hands . The highest prevalence of MRSA was found in meat samples in our study, which is comparable to a study conducted in Georgia  . In our  Georgia  and high Georgia . This himecA gene. Generally speaking, our results are consistent with studies that indicated the detection of staphylococci strains which were mecA positive but susceptible to cefoxitin . A similar study conducted in Egypt showed spa gene segment size ranges from 100, 200, 280, and 290 bp after PCR amplification of MRSA isolates , , and in Italy (253 bp), respectively , thus further in-depth genetic analysis covering the entire country is required including the detection of resistant genes , enterotoxins, pvl , SCCmec types , spa typing and MLST (Multilocus sequence typing). There is also a need for continuous tracking, and the introduction of improved management methods inside the food chain to reduce contamination of food with MRSA and the eventual spread of the bacteria.The current study concludes that raw meat in the twin cities of Pakistan was contaminated with pathogenic methicillin-resistant All datasets generated for this study are included in the article/AS and JK contributed to the idea or design of the research. JK compiled the data. AS wrote down the first draft of the manuscript and wrote the subsequent revisions of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Brominated flame retardants (BFRs) have been using to reduce the flammability of plastics contained in many products, such as household articles, furniture, mattresses, textiles or insulation. Considering the fact that these compounds may be released into the environment leading to the exposure of living organisms, it is necessary to study their possible effects and mechanisms of action. Proteins play a crucial role in all biological processes. For this reason, a simple model of human serum albumin (HSA) was chosen to study the mechanism of BFRs’ effect on proteins. The study determined interactions between selected BFRs, i.e., tetrabromobisphenol A (TBBPA), tetrabromobisphenol S (TBBPS), 2,4-dibromophenol , 2,4,6-tribromophenol  and pentabromophenol (PBP), and HSA by measurement of fluorescence of intrinsic tryptophan and absorbance of circular dichroism (CD). In addition, in order to understand the possible effect of these compounds in their native environment, the effect of BFRs on membrane proteins of human erythrocytes  was also assessed. Among bromophenols, PBP had the strongest oxidative effect on RBC membrane, and 2,4-DBP demonstrated the weakest fluorescence-quenching effect of both membrane tryptophan and HSA. By contrast to PBP, 2,4-DBP and 2,4,6-TBP caused spatial changes of HSA. We have observed that among all analyzed BFRs, TBBPA caused the strongest oxidation of RBC membrane proteins and the model HSA protein, causing reduction of fluorescence of tryptophan contained in them. TBBPA also changed albumin conformation properties, leading to impairment of the α-helix structure. However, TBBPS had the weakest oxidative effect on proteins among studied BFRs and did not affect the secondary structure of HSA. Brominated flame retardants (BFRs) are the compounds used to reduce flammability and/or retard combustion of organic materials, mostly of polymerized plastics. Despite increasing regulations and a ban on the use of selected BFRs, such as pentabrominated diphenylethers (PentaBDE), octabrominated diphenylethers (OctaBDE), decabrominated diphenylethers (DekaBDE) and hexabromocyclododecane (HBCD), the compounds of this family are still widely used on the market. BFRs are commonly used to prevent fires in electronic and electrical equipment, which accounts for more than 50% of their applications. In addition, BFRs are used to reduce the flammability of plastics in many products, such as household articles, furniture, mattresses, textiles or insulation .Tetrabromobisphenol A (TBBPA) has the largest share in the production of BFRs (approximately 60%). Based on in vitro studies it has been determined that this compound may be potentially harmful for the human organism. Due to the fact that TBBPA structure is similar to the structure of the thyroid hormone, thyroxine T4), and it is a derivative of another endocrine disruptor, bisphenol A (BPA), it is suspected that TBBPA may affect the hormonal system of living organisms, including humans . It is b, and it Our previous studies have demonstrated that BFRs, such as 2,4-dibromophenol , 2,4,6-tribromophenol , pentabromophenol (PBP), tetrabromobisphenol S (TBBPS), tetrabromobisphenol A (TBBPA) at very low concentrations caused an increase of reactive oxygen species (ROS) production , alteredMany reports have indicated that TBBPS could be a less toxic substitute for TBBPA. However, the latest scientific report indicated that TBBPS was also characterized by harmful activity, e.g., influencing the human neural stem cells’ (hNSCs) ability to self-renew and differentiate, which could account for neural developmental toxicity . TBBPS wProteins play a crucial role in all biological processes. They are catalysts, and are responsible for transport and storage of various molecules, play various mechanical, structural and signal functions, and much more. The tertiary and quaternary structure of proteins, or the spatial arrangement of the polypeptide chain and relative positions and interferences between protein units, influence physical and chemical properties of a molecule and its biological functions. Amino acid composition, or the primary structure, plays the decisive role in conformation of a protein molecule, by formation of hydrogen bonds in the polypeptide chain, leading to the formation of zones of, among others, alpha-helix and beta-sheet. Further interactions of side amino acid residues in various regions of the polypeptide molecule through hydrogen bonds, disulphide bridges, van der Waals forces, and bipolar interactions, lead to formation of the tertiary structure of the protein unit, the mere. Functional proteins are made of one to four units (meres). Relative arrangement of units forms the quaternary structure. Strength of bonds responsible for maintenance of a given structure decreases with increasing order of the structure. Proteins undergo conformational changes upon ligand binding . Those cTaking the above into consideration, it is important to determine the effects of BFRs on proteins, because changes in those macromolecules may be involved in the development of numerous diseases. A simple model of human serum albumin (HSA) was chosen to study the mechanism of BFRs’ action on proteins. HSA was selected as a model protein with a well-known structure, possessing one tryptophan residue in the subdomain IIA, which simplifies the analysis of fluorescence results. The protein also plays an important role in the process of detoxification of xenobiotics. It has been proven that transport, distribution and final neutralization of xenobiotics entering the organism often depend on HSA, as the best-known carrier protein in the cardiovascular system. Moreover, it is the major circulating antioxidant, able to scavenge large amounts of ROS and RNS . For thoPotential binding of BFRs to proteins or their indirect action on protein molecules, e.g., by ROS production, may cause structural and functional changes in proteins, and this may explain some of the pathological conditions observed in chronic BFR exposure. Therefore, for the first time, we have compared the effect of five BFRs on human RBC membrane proteins by measurement of tryptophan fluorescence they contain as well as on HSA. This study allowed us to present the effect of BFRs under native conditions and answer the question as to whether tested compounds were able to damage tryptophan residues contained in proteins of RBC lipid bilayer (native system) and in the chemical system consisting of only the HSA.It was found that analyzed compounds caused quenching of tryptophan fluorescence in HSA. Fluorescence loss with increasing concentration of analyzed BFRs was observed , while t0/F and the level of the quencher [Q], a Stern–Volmer graph was drawn for low (micromolar) concentrations of the studied compounds, which is recommended for this type of analysis. Then, using the correlation between F0 and F were fluorescence intensities in the absence and presence of quencher, Ksv was the Stern–Volmer constant or fluorescence quenching constant and [Q] is concentration of the quencher.It was observed that tryptophan fluorescence quenching caused by three bromophenols  was similar, evidenced by linearity of the plot. However, a non-uniform quenching was observed for TBBPA and TBBPS. Stern–Volmer plots for different BFRs are shown in 0/F coefficient  was noted for 2,4,6-TBP , followed by PBP , and finally by 2,4-DBP   . It was observed that TBBPA was the only compound causing statistically significant changes in HSA structure even at the concentration of 25 µg/mL. However, at the highest applied concentration of 50 µg/mL three compounds, namely TBBPA, 2,4-DBP and 2,4,6-TBP caused changes in the CD HSA spectrum . TBBPS (p < 0.01), and for 2,4,6-TBP to 15.33% (p < 0.05), compared to 13.03% observed for the control sample . A significant increase of random coil forms was also found for TBBPA and 2,4-DBP, to approximately 22% . Other bromophenols from 50 µg/mL caused a statistically significant reduction of fluorescence intensity, while TBBPS from 250 µg/mL decreased the studied parameter .Protein damage may be the result of a direct interaction with toxicants, or an indirect effect of ROS ,22. ReacFluorescence spectroscopy is a very efficient method for studying intramolecular interactions involving proteins . ProteinIn order to assess the effect of BFRs on protein in a chemical system, we have compared the effect of TBBPA, TBBPS, 2,4-DBP, 2,4,6-TBP and PBP on HSA. This type of study has been repeatedly conducted for drug–protein interaction , but verThe studied compounds caused a concentration-dependent, significant and consistent fluorescence quenching of HSA, which was the most probably due to high level of BFRs–protein interaction. However, BFRs did not cause a shift of emission maximum of HSA, which was centered at 342 nm. The results obtained suggested that the analyzed compounds did not alter the polarity in the vicinity of the fluorophores, especially in the vicinity of the only Trp-214 residue. It is important because Trp-214 is situated in the region of Sudlow’s site, which plays a crucial role in drugs binding ,27,28. MSV) plot was linear for HSA incubated with higher concentrations of TBBPA and TBBPS. Analysis of deviation of the directional coefficient “a” for TBBPA (0.0339 ± 0.0125) and for TBBPS (0.05691 ± 0.0065) showed no statistically significant differences (p = 0.119) for tryptophan fluorescence quenching intensity at higher concentrations  of these BFRs. If the Stern–Volmer plot is not linear, a higher complexity of the fluorophore quenching process may be discussed. At higher concentrations of both TBBPA and TBBPS, a static quenching may have become significant, showing the formation of a non-fluorescent complex with the quencher, or “instantaneous” adsorption of the emitted energy by a nearby  molecule of a quencher. The differences in the affinity and binding to HSA of the compounds studied are largely related to their molecular structure. Wang et al. phenol)), 2,4-DBP , PBP  were bought from LGC Standards (Germany). 2,4,6-TBP  and human serum albumin  were purchased from Sigma-Aldrich, Merck. Tetrabromobisphenol S sulfonylphenol) was synthetized in the Institute of Industrial Organic Chemistry in Warsaw, Poland. The compounds were dissolved in in dimethyl sulfoxide  to the final concentration of 0.4%. HSA was dissolved in PBS at a concentration of 5 µmol/L. The emission spectra were recorded from 300 to 440 nm after excitation at 295 nm with the maximum observed at 342 nm. The width of the excitation and emission slit was 5–10 nm. The measurement was carried out at room temperature (21 °C). BFRs in the concentrations from 0.1 to 50 µg/mL were added to the HSA solution, and fluorescence spectra were recorded . In addition, low micromolar concentrations of analyzed compounds (0.1–10 µM) were used to evaluate the mechanism of tryptophan fluorescence quenching and to plot Stern–Volmer curves. Control samples with and without DMSO were tested in order to exclude the possibility of fluorescence quenching by the solvent. Circular dichroism (CD) is a valuable technique for examining the structure of proteins in solution. CD refers to the differential absorption of 2 polarised components of equal magnitude, one rotating counter-clockwise  and the other clockwise  . CD specSince it was observed that DMSO impaired measurement of CD spectra, the compounds were dissolved in ethanol. Spectral data was expressed as the mean residue ellipticity (MRE). Protein secondary structure was evaluated using CD deconvolution.RBCs were isolated from buffy coat obtained from blood from healthy donors from the Regional Centre of Blood Donation and Blood Treatment . The RBCs’ isolation procedure was previously described by Jarosiewicz et al. (2017) . RBCs wiThe use of human blood (leucocyte buffy-coat) in the investigation of the effect of flame retardants on human erythrocytes has been approved by Bioethics Committee for Scientific Investigation, University of Lodz (agreement No. 7/KBBN-UŁ/II/2015).Protein damage was assessed based on the fluorescence measurement at 335 nm after excitation at 295 nm. Fluorescent properties of proteins are associated with the presence of aromatic amino acids (mainly tryptophan) in their structure. A decrease in fluorescence of a sample results from oxidative damage to tryptophan, thus allowing for the assessment of RBC membrane proteins damage [p < 0.05. All statistical analyses were performed using the STATISTICA software  and GraphPad Prism software data analysis program .The statistical analysis was performed using the one-way analysis of variance (ANOVA) followed by the Tukey post hoc test and presented as means ± standard deviation (SD). Differences were considered to be statistically significant at It was found that the structure of BFRs differing in the number of aromatic rings and the number of bromine atoms in the ring significantly affected their interaction with proteins.Bromophenols quenched tryptophan fluorescence in HSA linearly over the entire range of their analyzed concentrations (0.1–10 µM). The action of bromobisphenols was more complex and at their lower concentrations TBBPS had no effect on HSA fluorescensce up to 2.5 μM. Among analyzed bromophenols, PBP showed the strongest oxidative effect on RBC membrane , and comparable effect to 2,4,6-TBP in case of interactions with HSA . However, probably due to its structure, PBP did not penetrate nor disturbed the spatial structure of HSA. On the contrary, 2,4-DBP had the weakest oxidative effect among analyzed bromophenols, causing the most pronounced loss of the alpha-helix structure in HSA. Of all analyzed BFRs, TBBPA caused the strongest oxidative changes in RBC membrane proteins, and of the model HSA protein, causing reduction of fluorescence of tryptophan contained in them. This compound also changed albumin conformation properties, leading to impairment of the α-helix and rise of the content of the ß-sheet structure. On the other hand, TBBPS had the weakest oxidative effect on proteins, because it did not alter the secondary structure of HSA.


Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures  were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation. Based on the GLOBOCAN 2018 produced by the International Agency for Research on Cancer, a database that estimates the incidence and mortality of cancer (including 185 countries and 36 cancers), lung cancer incidence is high and was estimated to be 2.1 million new cases and 1.8 million deaths worldwide, representing 18.4% of all cancer cases [Clinical management highly depends on the histological subtype, as well as immunohistological (IHC) and genetic tumor characteristics . Two majMorphological evaluation of tissue sections remains the basis of histopathological diagnostics and directs the application of additional analyses . In someDigital pathology has emerged as an important tool, not only to review histopathological slides on a computer but also to use additional computer-assisted software to support routine diagnostics and research ,15,16,17In this study, we applied CNNs and evaluated their capability to classify the most common lung cancer subtypes—namely, SCLC, ADC, and SqCC. Moreover, we developed quality control (QC) measures to objectively detect cases that should be submitted for further evaluation.n = 80), ADC (n = 80) and SqCC (n = 80)—and skeletal muscle (n = 30) as a control was assembled from the archive from the Institute of Pathology, University Clinic Heidelberg with the support of the Tissue Biobank of the National Center for Tumor Diseases (NCT). Diagnoses were made according to the 2015 World Health Organization Classification of Tumors of the Lung, Pleura, Thymus, and Heart [A cohort of the three most frequent lung cancer subtypes—SCLC . Tumor areas of SCLC, ADC, and SqCC as well as from skeletal muscle were annotated by a pathologist (M.K.). Patches 100 × 100 µm (395 × 395 px) in size were generated within QuPath, and the tumor-associated image patches were exported to the local hard drive . To ensuThe following hardware were used for all calculations: Lenovo Workstation p72, CPU Intel(R) Xeon(R) E-2186 M, 2.90 GHz , GPU 128 GB DDR4 RAM, GPU NVIDIA Quadro P5200 with Max-Q Design 16 GB RAM . The following software were used: x64 Windows for Workstations , R  and RStudio  with the packages Keras (v.2.2.5.0), TensorFlow (v.2.0.0) and Tidyverse (v.1.3.0).To ensure reliable results, image patches were randomly separated into training (60% of patients), validation (20% of patients), and test sets (20% of patients). All image patches from a patient were in one of the sets only. These subsets were not changed during the analyses.Our setup using keras and tensorflow in R analytical software allowed us to choose a subset of different network architectures among the hundreds of network architectures available. After a literature review, three different commonly used and previously published CNN architectures were chosen and applied for the analysis. The results were subsequently compared. The CNNs were VGG16, InceptionV3 and InceptionResNetV2 ,33,34,35n = 80), ADC (n = 80), SqCC (n = 80), and skeletal muscle (n = 30) were successfully identified, retrieved, assembled in a TMA, stained, and scanned. Identification of the tumor-containing region resulted in a total of 12,472 extracted 100 × 100 µm (395 × 395 px) image patches. The aim of extracting at least 10 image patches per patient was achieved in all but three SCLC cases, which were still included in the analysis. The number of extracted patches is displayed in Cases from SCLC  were compared. A batch size of 16 had optimal classification accuracy metrics, i.e., 95% in the training set and 89% in the validation set D.As a slight overfitting was noted, different dropout rates  were evaluated. Compared with the other values, no overfitting was noted with a drop-out rate of 0.5 and a classification accuracy of 88% and 89% in the training and validation sets, respectively E.The variable parameters of the final CNN model and its performance on the training and validation sets are shown in n = 192; input image size, 256 × 256 px; image augmentation, yes; batch size, n = 16; dropout rate, 0.5; loss function, categorical crossentropy; optimizer, RMSProp; learning rate, 0.00002; and output metrics, accuracy and loss.The output parameter loss and classification accuracy are shown for the training and validation sets over 20 epochs. The final CNN model parameters were as follows: CNN architecture, InceptionV3; trainable weights, The final trained CNN model was evaluated on an independent test set. The output of this evaluation was a probability for every single image patch to correspond to one of the four trained classes. However, as an image patch-based classification is not suitable for routine application , two QC parameters were introduced to ensure a high level of classification certainty—(i) a minimum probability for the image patches to fall into one class (image patch QC) and (ii) a minimal proportion of images that need to be classified as one category (case QC). The principle of the two QC categories is shown in First, the image patch QC was increased from 50% to 90% in 10% increments. With increasing values for the image patch QC, the number of image patches that did not pass the QC increased from 1/2448 (<1%) at an image patch QC of 50% to 386/2448 (16%) at an image patch QC of 90%. Simultaneously, the classification accuracy increased from 89% to 95% in the whole cohort. Most misclassifications were found between ADC and SqCC . The classification results separated for the whole cohort, for the three lung cancer subtypes, and for the NSCLC subgroup are displayed in Second, case QC was evaluated in combination with image patch QC from 50% to 90% in 10% increments. The results for the whole cohort are displayed in The morphological evaluation of tissue specimens in lung cancer diagnostics is the basis for further molecular testing and therapy stratification . CriteriIn this study, we analyzed whether a CNN-model (InceptionV3 CNN) could be used to differentiate the most common lung cancer subtypes—SCLC, ADC, and SqCC. To check the plausibility of the results, skeletal muscle was also included in the analysis. Histologically, the distinction of skeletal muscle and the three tumor entities is unambiguous. Furthermore, high classification accuracies were expected for the distinction between SCLC and NSCLC, as the cell size is commonly very different . Only inIn this study, we used a TMA to extract the image patches for several reasons. First, the tumor-containing area of each patient is comparable . Second,The creation of image patches from a scanned image is necessary, as CNN can process only limited image sizes . The sepIn the past, various CNN architectures and modifications have been developed, and some show a high classification accuracy in the ImageNet dataset ,50,51. HThe final model was robust and reached an image patch classification accuracy of 88% in the training as well as in the validation set which is comparable to previous studies using histological images ,57 MainlThe limitations of our study are the sample size, the number of extracted image patches in some cases, the number of included entities and the process for hyperparameter tuning. Herein, we examined 80 cases per lung cancer entity. Based on the random separation into training, validation and test sets, only 48 tumors were included in the training set. ADC and SqCC may be morphologically very different, and many variants are recognized in the current World Health Organization classification . FurtherIn summary, we trained and optimized a CNN model to reliably classify the three most common lung cancer subtypes. Moreover, we established QC measures to objectively identify cases that need further IHC validation for reliable entity subtyping. Our results highlight the potential and limitations of CNN image classification models for morphology-based tumor classification.