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In a hospital, keeping track of patients and their needs is a multi-faceted nightmare. Each department has its own intake and check-out process, and all of this information must be in one central place. That’s why there are so many hospitals that are struggling to confirm the organization of patient data. There are several of Machine Learning algorithms that can help to optimize patient flow in hospitals, provide a real-time presentation of department workflow. This editorial will examine some of the most effective ML algorithms to solve these issues. Before we dive directly into the solutions, it is important to understand how ML can help specifically in the healthcare industry. The increased adoption of ML stipulates a wide range of new technologies and applications that would transform medicine. These healthcare developments would help providers to offer care tailored to the individual needs of patients, making treatment more effective and affordable. Although the actual impact of the algorithms on healthcare is still a subject of future research, the expected outcomes are huge. Before analyzing how they work, it is important to explain the terminology. Specifically: data mining is the manner of discovering new data or knowledge from huge amounts of data. The major focus of data mining is made on unstructured data, as e.g. medical records, social media posting or images. Machine learning implies a computer program that is trained on data to find patterns and make predictions.1 There are many types of ML algorithms, but they all have three steps in common: • Training: create a model using the data. • Use the model to make predictions. • Tune the model to get the best results. Natural Language Processing (NLP) is a type of machine learning that allows computers to understand human language.2 Health care providers use NLP to meet patients’ needs, goals, and expectations. One of the many problems in hospitals is the inability to manage patient flows through various departments and check-out points. The problem is that when patients arrive at a hospital, they can be instructed to go to the Emergency Department. They can also be told to go to a different department based on the results. In spite of the fact that it is necessary for there to be a single source of information, regardless of the department, in order to ensure that both patients and staff have access to the same level of information.3 It would be ideal to have a centralized location that provides information on the department's current availability, wait times, and staff members' status. In order to enhance the efficiency of patient flow within the hospital, administrators and healthcare providers can utilize machine learning algorithms to gather information regarding patients' arrival, departure, and current condition. In addition, it is able to evaluate the condition of each individual patient and give staff with information in real time. This strategy makes it easier to control the flow of patients in an effective manner, which in turn enables staff members to concentrate on the activities that are most important to them. There are a great number of data sources that can be operated in order to collect information regarding the conditions of patients. To ensure that activities are carried out in the most effective manner possible, it is essential that staff members have instant access to these resources. Natural language processing, often known as NLP, is a method that is utilized for the retrieval of information that is very effective. Natural Language Processing (NLP) has made it possible for computers to grasp human language. This capability was first introduced in 2005. They are able to collect vital information from a patient's report and incorporate it into the centralized system in a seamless manner.4 Because of this, the system is able to collect and store information about the diagnoses, treatments, prescriptions, and other pertinent information pertaining to patients. In addition to providing patients and staff members with real-time updates on the situation, the data may be employed to provide precise estimations of the amount of time that patients will be required to wait. In addition to this, it is able to recognize and assess patterns and trends within the process of the department, thus allowing the required adjustments to be made in order to improve efficiency. After collecting data from patients, it is possible to analyses trends and patterns within the data. The investigation has the potential to improve patient care by highlighting areas for improvement. Using an acceptable sample size, it is possible to make reliable predictions about the likelihood of specific occurrences. The use of a machine learning algorithm allows for the assessment of the likelihood that persons with specified conditions will be discharged or admitted to a medical facility.5 The use of these algorithms is critical in giving invaluable assistance to workers in the performance of their everyday activities, as well as predicting and anticipating the state of patients. The rapid examination of data has various advantages, notably in terms of minimizing wait times. The provision of timely help is an important part of controlling patient flow because it has a direct impact on patient satisfaction and likely to recommend the healthcare institution. The implementation of an automated system for data collection and analysis can assist hospitals in attaining transparency. To augment transparency within the healthcare facility, administration may elect to employ machine learning algorithms to produce status dashboards.6 This type of dashboard has the capability to deliver instantaneous updates regarding the condition of facilities and departments. It can provide details such as staff availability, wait times, and the individual patient loads of each staff member. There are specific situations in which the implementation of a decentralized system is required to assure hospital-wide transparency. Particularly designed chatbots may be employed in such circumstances to collect data and provide immediate transparency. A chatbot that is purpose-built for the hospital environment can efficiently respond to fundamental inquiries, collect critical data, and improve patient transparency. By employing ML algorithms, the efficacy of patient flow can be significantly enhanced. Conflict of interest: None declared.

In this computational approach, four small novel acceptor molecules of A2-D-A1-D-A2 type have been designed (S1-S4) by the replacement of terminal acceptor groups (A2) of R molecule with four different types of electron accepting-units. The photovoltaic aspects of molecules have been studied by using DFT based method i.e. B3LYP/6–31 G (d, p). The energy level of ground and excited state, bandgap (Egap), excitation energy, transition density matrix (TDM), absorption maxima (λmax), light-harvesting efficiency (LHE), the density of states (DOS), open circuit voltage (VOC) and many other optoelectronic properties of molecules have been investigated in this research. Out of all the newly suggested molecules, S2 has exhibited the highest red-shift in λmax (815 nm), the lowest excitation energy (1.52 eV), small band gap and ionization potential, and the highest oscillator strength (1.97). The VOC of the studied acceptor molecules was calculated with regard to the PTB7-Th donor, and the findings showed that all of the newly suggested molecules displayed a higher VOC and a greater FF than that of R molecule. Consequently, the findings of these calculations show that our computational investigations give profound insights to support a realistic implementation of OSCs with the accepted acceptor molecules examined.

Objective: To develop a Raman spectroscopy-based analytical model for quantification of solid dosage forms of active pharmaceutical ingredient (API) of Atenolol. Significance: For the quantitative analysis of pharmaceutical drugs, Raman Spectroscopy is a reliable and fast detection method. As part of this study, Raman Spectroscopy is explored for the quantitative analysis of different concentrations of Atenolol. Methods: Various solid-dosage forms of Atenolol were prepared by mixing API with excipients to form different solid-dosage formulations of Atenolol. Multivariate data analysis techniques, such as Principal Component Analysis (PCA) and Partial least square regression (PLSR) were used for the qualitative and quantitative analysis, respectively. Results: As the concentration of the drug increased in formulation, the peak intensities of the distinctive Raman spectral characteristics associated with the API (Atenolol) gradually increased. Raman spectral data sets were classified using PCA due to their distinctive spectral characteristics. Additionally, a prediction model was built using PLSR analysis to assess the quantitative relationship between various API (Atenolol) concentrations and spectral features. With a goodness of fit value of 0.99, the root mean square errors of calibration (RMSEC) and prediction (RMSEP) were determined to be 1.0036 and 2.83 mg, respectively. The API content in the blind/unknown Atenolol formulation was determined as well using the PLSR model. Conclusions: Based on these results, Raman spectroscopy may be used to quickly and accurately analyze pharmaceutical samples and for their quantitative determination. [ABSTRACT FROM AUTHOR]

The purpose of this study is to describe the outbreak of a novel coronavirus (COVID-19) and review the computational model, which was based on non-linear ordinary differential equations. The main idea developed was to describe the formulation, integration, and application of mathematical models by using data from China, Austria, Poland, and France. The Logistic Model and its solution Sigmoid Curve, and the SIR Model were used for the determination. After taking the measured data, the total number of cases was predicted, and it was found that the results were obtained in good approximation to the measured results. To predict the number of deaths over time, generalized models were developed in response to COVID-19.

We report herein a two-step one-pot strategy for the synthesis of sulfonamides/N-sulfonyl succinic esters from N-sulfonyl succinimide by using potassium fluoride as a promoter. This approach involves the in situ generation of the N-sulfonyl succinimide additional product by KF, further reacted with alcohols to yield N-sulfonyl succinic esters by C-N cleavage, whereas sulfonamides are obtained via hydrolysis. Moreover, the S-N bond in sulfonamide displays inert behavior, which results in KF activating the carbonyl group of succinimide. Notably, this new approach employs N-sulfonyl succinimide to give sulfonamides/N-sulfonyl succinic esters in good to excellent yields. This protocol can be efficiently utilized in the synthesis of broad sulfonamides/N-sulfonyl succinic esters under a simple potassium fluoride promoter and metal-free conditions. [ABSTRACT FROM AUTHOR]

Objectives: To assess knowledge, attitude and practice of medical and paramedical staff about cervical cancer as well as its screening and prevention. Methods: The descriptive cross-sectional study was conducted at the Jinnah Post-graduate Medical Centre, Karachi, from March 1 to August 30, 2019, and comprised women medical and paramedical staff randomly selected from different specialties. Data was collected using a structured questionnaire. Data was analysed using SPSS 20. Results: Of the 347 participants 144(41.5%) were nurses and 203(58.5%) were doctors. The overall mean age was 26.22±6.38 years. Of the total, 108(30%) respondents were married and 239(68%) were single. Overall, 239(68.8%) were well aware of Pap smear being the screening test; 85(24.5%) were aware of the true guidelines to repeat the test; 152(43.8%) had an idea of the exact use of visual Inspection with acetic acid; 61(17.6%) had got a Pap smear done; and 156(45%) thought they were at risk of developing carcinoma cervix. The common risk factors identified were multiple sexual partners 254(73.2%), age at first sexual intercourse 160(46%), smoking 131(37.8%), foul-smelling discharge 221(63.7%), and post-coital bleeding 231(66.6%). Conclusion: Cervical cancer prevalence is rising due to inadequate knowledge and awareness among healthcare personals. Improvement can be brought by regular use of Pap smear. Key Words: Cervical cancer, Pap smear, Healthcare professionals, Visual inspection with acetic acid.

Two new organoselenium analogs, (3,3'-(1,2-phenylenebis(methylene))bis(2-selenoxo-2,3-dihydro-1H-imidazole-3,1-diyl))bis(methylene) dibenzoate (III) and 3,3'-(1,2-phenylenebis(methylene))bis(1-benzyl-1H-imidazole-2(3H)-selenone) (V), were derived from newly synthesized organic salts 1,1'-(1,2-phenylenebis(methylene))bis(3-(2-phenoxyacetyl)-1H-imidazol-3-ium) chloride (II) and 1,1'-(1,2-phenylenebis(methylene))bis(3-benzyl-1H-imidazol-3-ium) chloride (IV), and each was characterized by various analytical techniques, such as Fourier-Transform Infrared Spectroscopy (FT-IR), UV–visible, and Nuclear Magnetic Resonance (NMR) spectroscopy, as well as Ultra-high performance liquid chromatography mass spectrometry/Photodiode array (UHPL-MS/PDA). All synthesized compounds were tested for their antioxidant and anticancer potential. Very good radical scavenging results were obtained for salts II and IV, with cell viability values of 84.6±3.5 and 56.7±5.5%, respectively, compared to their organoselenium adducts. All synthesized products showed significant activity against MCF-7 breast cancer cells, but compounds II and III showed better results, with cell viability values of 40.5±2.0% and 34.4±1.5%, respectively.

Herein, we reported 36 novel nifuroxazide derivatives based on 4 or 5‐nitropyrrole skeleton for the first time. The influence of the benzyl substituents on the pyrrole ring and/or hydroxyl substituent on the benzene ring for the anticancer activity was investigated. Most of the designed derivatives were active at micromolar or submicromolar concentrations. The most promising compounds, namely derivatives (E)‐2,3‐dihydroxy‐N′‐((5‐nitro‐1‐(4‐(trifluoromethyl)benzyl)‐1H‐pyrrol‐2‐yl)methylene) benzohydrazide (18), (E)‐N′‐((1‐(4‐fluorobenzyl)‐5‐nitro‐1H‐pyrrol‐2‐yl) methylene)‐2,3‐dihydroxybenzohydrazide (24), and (E)‐N′‐((1‐(3‐fluorobenzyl)‐5‐nitro‐1H‐pyrrol‐2‐yl) methylene)‐2,3‐dihydroxybenzohydrazide (30), exhibited better antitumor activity than nifuroxazide in vitro, with IC50 values ranging from 0.80 to 5.18 μM on CNE2 (human nasopharyngeal carcinoma cell line) and SUNE1 (human nasopharyngeal carcinoma cell line). Docking results indicated that compounds 24 and 30 interacted with the SRC homology 2 (SH2) and carboxyl‐terminal transactivation domain of STAT3 (the signal transducer and activator of transcription 3), with binding energies ranging from −3.98 to −3.88 kcal/mol, and exhibited similar binding modes and energies to nifuroxazide. Interestingly, the trifluoromethyl substituted 18 interacts in the coiled‐coil, DNA‐binding and linker domain of STAT3, with a binding energy of −4.16 kcal/mol. [ABSTRACT FROM AUTHOR]

To use Raman Spectroscopy for qualitative and quantitative evaluation of pharmaceutical formulations of active pharmaceutical ingredient (API) of Cephalexin. Raman Spectroscopy is a noninvasive, nondestructive, reliable and rapid detection technique used for various pharmaceutical drugs quantification. The present study explores the potential of Raman Spectroscopy for quantitative analysis of pharmaceutical drugs. For qualitative and quantitative analysis of Cephalexin API, various standard samples containing less and more concentration of API than commercial tablet was prepared. To study spectral differences, the mean plot of all the samples was prepared. For qualitative analysis, Principal Component Analysis (PCA) and for quantitative analysis Partial Least Square Regression analysis (PLSR) was used. Both of these are Multivariate data analysis techniques and give reliable results as published in previous literature. PCA model distinguished all the Raman Spectral data related to the various Cephalexin solid dosage formulations whereas the PLSR model was used to calculate the concentration of different unknown formulations. For the PLSR model, RMSEC and RMSEP were determined to be 3.3953 and 3.8972, respectively. The prediction efficiency of this built PLSR model was found to be very good with a goodness of the model value (R2) of 0.98. The PLSR model also predicted the concentrations of Cephalexin formulations in the blind or unknown sample. These findings demonstrate that the Raman spectroscopy coupled to PLSR analysis could be regarded as a fast and effectively reliable tool for quantitative analysis of pharmaceutical drugs. [ABSTRACT FROM AUTHOR]

Parameters affecting extraction of natural colorants from five binary mixtures of floral parts of Callistemon citrinus and Tagetes erecta L., like ratio of each flower in the binary mixture of powdered floral parts, amount of powdered binary mixture to liquor ratio and extraction time, were optimized for dyeing of cotton fabric. Optimum dyeing was obtained at 60°C with 70 g/L exhausting agent (Na2SO4), material to liquor ratio of 1:50 along with dyeing time of 100 min. The dyed fabric revealed good anti-bacterial activity and excellent ultraviolet protection factor (UPF).

Fall armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), is a destructive pest of a wide array of agricultural and horticultural crops worldwide. This in vitro research assessed the combined effect of methanolic extracts of indigenous flora of Soone Valley (Khushab, Pakistan) and nine commonly used synthetic insecticides against 3rd instar larvae of S. frugiperda using the leaf-dip bioassay method. Toxicity bioassays with twelve plant extracts revealed that the extracts of Withania somnifera (L.) Dunal, Sophora mollis (Royle) Baker and Rhazya stricta Decne. were the most effective, exhibiting minimum LC50 and LT50 values. Bioassays with synthetic insecticides revealed a significantly higher mortality of S. frugiperda larvae by emamectin benzoate (45%), chlorpyrifos (40%) and chlorantraniliprole (38%). Further bioassays with 10 binary combinations of these most effective botanical and synthetic insecticides showed that seven pesticidal combinations exhibited synergistic toxicity, and three combinations comprising emamectin benzoate exhibited an additive effect on the mortality of S. frugiperda larvae. GC–MS analyses of methanolic extracts of W. somnifera, S. mollis and R. stricta revealed 1,2,4-trimethyl-benzene and 3,5-dimethyl-octane, 1-ethyl-2-methyl-benzene, and 1-monolinoleoylglycerol trimethylsilyl ether, decane, and lupeol as major bioconstituents, respectively. Our results demonstrated that combining botanicals with synthetic insecticides can synergize their toxicity against S. frugiperda larvae, suggesting their potential incorporation into future IPM programs against S. frugiperda and other lepidopterous pests.

Purpose: To evaluate the initial experience and outcome of photo-selective vaporization of the prostate (PVP) for benign prostatic hyperplasia (BPH) in Pakistan with the use of a 980 nm diode laser. Materials and Methods: A prospective study was performed from November 2016 to December 2017. A total of 100 patients diagnosed with bladder outlet obstruction secondary to BPH who planned for PVP were enrolled in the study. PVP was carried out with a diode laser at 980 nm (Biolitec Diode 180W laser) in a continuous wave with a 600 nm (twister) fiber. Baseline characteristics and perioperative data were compared. Postoperative outcomes were evaluated by International Prostate Symptom Score (IPSS), post void residual (PVR) and maximum urinary flow rate (Qmax) at 3 and 6 months after surgery. Results: The mean age was 65.82±10.42, mean prostate size was 67.35±16.42, operative time was 55.85±18.01 and total energy was 198.68±49.12 kJ. At 3 months and 6 months, significant improvements were noted (p

The current study is to evaluate the patient facing problems by applying Late Acceptance Hill Climbing Algorithm (LAHC) in hospital settings. The recent proposed procedure of LAHC is based on metaheuristic algorithm which is linked with one-point clarification method. Patient’s satisfaction regarding the performance of the hospital is a composite mechanism. The optimization procedure is connected with NP-hard problems which is practically related to the problems faced by patients. These problems are concerned with assigning the group of patients visiting the hospital for receiving healthcare services. The common issues faced by patients include communication gap, response time to attend patients, early symptomatic relief, getting proper advice for dosage and usage of medicines, clean hospital environment. Moreover, patient education and guidance before discharge from hospital is also the missing element. The suggested algorithm of LAHC to PFP is developed and it has two phases: the first phase includes providing the initial feasible solution using communication-oriented methodology. The second phase uses three neighborhood framework which are implanted inside the segment of PFP based on LAHC to additionally upgrade the underlying feasible solution of the introductory phase.

Objectives Evaluation of dynamic occlusal parameters can help dentists to understand the association of occlusion with the preferred chewing side (PCS) and enable optimal restoration of masticatory efficiency. This study evaluated the association between PCS and dynamic occlusal parameters. Methods One hundred participants (50 each, right and left PCS) were included in this study. PCS was determined by the visual spot-checking method, and occlusal analysis was conducted by T Scan. Occlusal parameters evaluated included initial contact, center of force, tooth contact area, occlusal interferences, and occlusal time. Data were assessed by chi-squared test and eta correlation coefficient. Results Initial contact towards the PCS was observed in 68% and 70% of right and left unilateral chewers, respectively. Likewise, center of force towards the PCS was present in 72% and 66% of right and left unilateral chewers, respectively. A larger area of tooth contact was predominant towards the PCS in 70% and 72% of right and left unilateral chewers, respectively. Chi-squared analysis showed a strong positive correlation between PCS and occlusal parameters at maximum intercuspation. Eta correlation coefficients between PCS and occlusal interferences, as well as centric and eccentric occlusion, revealed negligible associations. Conclusion Dynamic occlusal parameters may be associated with PCS.