Natural agents as wound-healing promoters.

The management of acute and chronic wounds resulting from diverse injuries poses a significant challenge to clinical practices and healthcare providers. Wound healing is a complex biological process driven by a natural physiological response. This process involves four distinct phases, namely hemostasis, inflammation, proliferation, and remodeling. Despite numerous investigations on wound healing and wound dressing materials, complications still persist, necessitating more efficacious therapies. Wound-healing materials can be categorized into natural and synthetic groups. The current study aims to provide a comprehensive review of highly active natural animal and herbal agents as wound-healing promoters. To this end, we present an overview of in vitro, in vivo, and clinical studies that led to the discovery of potential therapeutic agents for wound healing. We further elucidated the effects of natural materials on various pharmacological pathways of wound healing. The results of previous investigations suggest that natural agents hold great promise as viable and accessible products for the treatment of diverse wound types.

Aging of the medial olivocochlear reflex and associations with speech perception.

The medial olivocochlear reflex (MOCR) modulates cochlear amplifier gain and is thought to facilitate the detection of signals in noise. High-resolution distortion product otoacoustic emissions (DPOAEs) were recorded in teens, young, middle-aged, and elderly adults at moderate levels using primary tones swept from 0.5 to 4 kHz with and without a contralateral acoustic stimulus (CAS) to elicit medial efferent activation. Aging effects on magnitude and phase of the 2f1-f2 DPOAE and on its components were examined, as was the link between speech-in-noise performance and MOCR strength. Results revealed a mild aging effect on the MOCR through middle age for frequencies below 1.5 kHz. Additionally, positive correlations were observed between strength of the MOCR and performance on select measures of speech perception parsed into features. The elderly group showed unexpected results including relatively large effects of CAS on DPOAE, and CAS-induced increases in DPOAE fine structure as well as increases in the amplitude and phase accumulation of DPOAE reflection components. Contamination of MOCR estimates by middle ear muscle contractions cannot be ruled out in the oldest subjects. The findings reiterate that DPOAE components should be unmixed when measuring medial efferent effects to better consider and understand these potential confounds.

Toxicity prediction of nanoparticles using machine learning approaches.

Nanoparticle toxicity analysis is critical for evaluating the safety of nanomaterials due to their potential harm to the biological system. However, traditional experimental methods for evaluating nanoparticle toxicity are expensive and time-consuming. As an alternative approach, machine learning offers a solution for predicting cellular responses to nanoparticles. This study focuses on developing ML models for nanoparticle toxicity prediction. The training dataset used for building these models includes the physicochemical properties of nanoparticles, exposure conditions, and cellular responses of different cell lines. The impact of each parameter on cell death was assessed using the Gini index. Five classifiers, namely Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network, were employed to predict toxicity. The models' performance was compared based on accuracy, sensitivity, specificity, area under the curve, F measure, K-fold validation, and classification error. The Gini index indicated that cell line, exposure dose, and tissue are the most influential factors in cell death. Among the models tested, Random Forest exhibited the highest performance in the given dataset. Other models demonstrated lower performance compared to Random Forest. Researchers can utilize the Random Forest model to predict nanoparticle toxicity, resulting in cost and time savings for toxicity analysis.

Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review.

BACKGROUND AND OBJECTIVE: Pharmacokinetic studies encompass the examination of the absorption, distribution, metabolism, and excretion of bioactive compounds. The pharmacokinetics of drugs exert a substantial influence on their efficacy and safety. Consequently, the investigation of pharmacokinetics holds great importance. However, laboratory-based assessment necessitates the use of numerous animals, various materials, and significant time. To mitigate these challenges, alternative methods such as artificial intelligence have emerged as a promising approach. This systematic review aims to review existing studies, focusing on the application of artificial intelligence tools in predicting the pharmacokinetics of drugs. METHODS: A pre-prepared search strategy based on related keywords was used to search different databases (PubMed, Scopus, Web of Science). The process involved combining articles, eliminating duplicates, and screening articles based on their titles, abstracts, and full text. Articles were selected based on inclusion and exclusion criteria. Then, the quality of the included articles was assessed using an appraisal tool. RESULTS: Ultimately, 23 relevant articles were included in this study. The clearance parameter received the highest level of investigation, followed by the  area under the concentration-time curve (AUC) parameter, in pharmacokinetic studies. Among the various models employed in the articles, Random Forest and eXtreme Gradient Boosting (XGBoost) emerged as the most commonly utilized ones. Generalized Linear Models and Elastic Nets (GLMnet) and Random Forest models showed the most performance in predicting clearance. CONCLUSION: Overall, artificial intelligence tools offer a robust, rapid, and precise means of predicting various pharmacokinetic parameters based on a dataset containing information of patients or drugs.

Prediction of ovarian cancer using artificial intelligence tools.

Purpose: Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods. These methods provide a tool for predicting ovarian cancer according to the characteristics and conditions of each person. Method: In this study, a data set included records related to 171 cases of benign ovarian tumors, and 178 records related to cases of ovarian cancer were analyzed. The data set contains the records of blood test results and tumor markers of the patients. After data preprocessing, including removing outliers and replacing missing values, the weight of the effective factors was determined using information gain indices and the Gini index. In the next step, predictive models were created using random forest (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN) models. The performance of these models was evaluated using the 10-fold cross-validation method using the indicators of specificity, sensitivity, accuracy, and the area under the receiver operating characteristic curve. Finally, by comparing the performance of the models, the best predictive model of ovarian cancer was selected. Results: The most important predictive factors were HE4, CA125, and NEU. The RF model was identified as the best predictive model, with an accuracy of more than 86%. The predictive accuracy of DT, SVM, and ANN models was estimated as 82.91%, 85.25%, and 79.35%, respectively. Various artificial intelligence (AI) tools can be used with high accuracy and sensitivity in predicting ovarian cancer. Conclusion: Therefore, the use of these tools can help specialists and patients with early, easier, and less expensive diagnosis of ovarian cancer. Future studies can leverage AI to integrate image data with serum biomarkers, thereby facilitating the creation of novel models and advancing the diagnosis and treatment of ovarian cancer.

Optimization of the Synthesis and Radiolabeling of ZIF-8 Nanoparticles.

Background: Lately, there has been increasing interest in the benefits of metal-organic frameworks, and among them, zeolitic imidazolate frameworks (ZIF - 8) stand out as one of the most commonly employed systems owing to their unique characteristics. Objectives: Given that properties like particle size play a key role in biomedical applications of nanoparticles, optimizing the synthesis conditions becomes crucial. Additionally, it is essential to label these nanoparticles to track them effectively within the body. Methods: Zeolitic imidazolate frameworks nanoparticles were synthesized under various conditions, including high and room temperature, using two different solvents: Water and methanol. Modifications were made to the reaction temperature and the ratio of reactants to improve the outcomes. Particle size and size distribution were assessed in all conditions. Additionally, the radiolabeling of nanoparticles was examined using four different methods to identify the method with the highest efficiency and radiochemical purity. Results: The optimum conditions for ZIF-8 synthesis were determined at 50°C using methanol as the solvent. A reactant weight ratio of 1: 2 (zinc nitrate to 2-methylimidazole) was utilized. The most effective radiolabeling approach involved using tin chloride as a reducing agent, with the reaction mixture maintained at a temperature of 70°C for 30 minutes. Conclusions: In this study, the optimum conditions were successfully identified for synthesizing and labeling ZIF-8 nanoparticles. These nanoparticles have the potential to serve as effective carriers for diagnostic and therapeutic agents.

Angiographic evaluation of coronary artery disease in diabetic patients with and without end-stage kidney disease.

The objective of the present investigation was to compare the coronary angiography results in diabetic patients with and without end-stage kidney disease (ESKD). We included prolonged diabetic patients with ESKD (93 patients) and without ESKD (control group, 126 patients). Angiography of the coronary arteries was performed on all patients. Our results revealed that the ESKD patients tended to have a higher degree of coronary artery stenosis in all parts of LAD (p = 0.001, 0.024, and 0.005), proximal and distal RCA (p = 0.013, and 0.008), and proximal and distal LCX artery (p = 0.001, 0.008) than non-ESKD patients. Furthermore, we found that the ESKD group had higher significant coronary artery stenosis in the LAD artery (60.5% vs. 39.5%, p < 0.001), RCA (60.3% vs. 39.7%, p < 0.001), LCX artery (79.5% vs. 20.5%, p < 0.001), and LMCA (84.6% vs 15.4%, p = 0.002) compared to control group. There was a greater prevalence of multiple vessels coronary artery disease (≥ two) among ESKD patients (29%), compared with the non-ESKD group (16.8%, p < 0.001). Significant coronary artery stenosis was meaningfully higher in asymptomatic diabetic ESKD patients on hemodialysis than non-ESKD diabetic patients. Coronary angiography may be beneficial in diabetic patients with ESKD regardless of whether they have ischemic symptoms with low complication rate through radial access.

Perceptual learning for speech in noise after application of binary time-frequency masks.

Ideal time-frequency (TF) masks can reject noise and improve the recognition of speech-noise mixtures. An ideal TF mask is constructed with prior knowledge of the target speech signal. The intelligibility of a processed speech-noise mixture depends upon the threshold criterion used to define the TF mask. The study reported here assessed the effect of training on the recognition of speech in noise after processing by ideal TF masks that did not restore perfect speech intelligibility. Two groups of listeners with normal hearing listened to speech-noise mixtures processed by TF masks calculated with different threshold criteria. For each group, a threshold criterion that initially produced word recognition scores between 0.56-0.69 was chosen for training. Listeners practiced with one set of TF-masked sentences until their word recognition performance approached asymptote. Perceptual learning was quantified by comparing word-recognition scores in the first and last training sessions. Word recognition scores improved with practice for all listeners with the greatest improvement observed for the same materials used in training.

Nanoparticles labeled with gamma-emitting radioisotopes: an attractive approach for in vivo tracking using SPECT imaging.

Providing accurate molecular imaging of the body and biological process is critical for diagnosing disease and personalizing treatment with the minimum side effects. Recently, diagnostic radiopharmaceuticals have gained more attention in precise molecular imaging due to their high sensitivity and appropriate tissue penetration depth. The fate of these radiopharmaceuticals throughout the body can be traced using nuclear imaging systems, including single-photon emission computed tomography (SPECT) and positron emission tomography (PET) modalities. In this regard, nanoparticles are attractive platforms for delivering radionuclides into targets because they can directly interfere with the cell membranes and subcellular organelles. Moreover, applying radiolabeled nanomaterials can decrease their toxicity concerns because radiopharmaceuticals are usually administrated at low doses. Therefore, incorporating gamma-emitting radionuclides into nanomaterials can provide imaging probes with valuable additional properties compared to the other carriers. Herein, we aim to review (1) the gamma-emitting radionuclides used for labeling different nanomaterials, (2) the approaches and conditions adopted for their radiolabeling, and (3) their application. This study can help researchers to compare different radiolabeling methods in terms of stability and efficiency and choose the best way for each nanosystem.

Systematic review of content analysis algorithms based on deep neural networks.

Today according to social media, the internet, Etc. Data is rapidly produced and occupies a large space in systems that have resulted in enormous data warehouses; the progress in information technology has significantly increased the speed and ease of data flow.text mining is one of the most important methods for extracting a useful model through extracting and adapting knowledge from data sets. However, many studies have been conducted based on the usage of deep learning for text processing and text mining issues.The idea and method of text mining are one of the fields that seek to extract useful information from unstructured textual data that is used very today. Deep learning and machine learning techniques in classification and text mining and their type are discussed in this paper as well. Neural networks of various kinds, namely, ANN, RNN, CNN, and LSTM, are the subject of study to select the best technique. In this study, we conducted a Systematic Literature Review to extract and associate the algorithms and features that have been used in this area. Based on our search criteria, we retrieved 130 relevant studies from electronic databases between 1997 and 2021; we have selected 43 studies for further analysis using inclusion and exclusion criteria in Section 3.2. According to this study, hybrid LSTM is the most widely used deep learning algorithm in these studies, and SVM in machine learning method high accuracy in result shown.

Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review.

Nanoparticles have been used extensively in different scientific fields. Due to the possible destructive effects of nanoparticles on the environment or the biological systems, their toxicity evaluation is a crucial phase for studying nanomaterial safety. In the meantime, experimental approaches for toxicity assessment of various nanoparticles are expensive and time-consuming. Thus, an alternative technique, such as artificial intelligence (AI), could be valuable for predicting nanoparticle toxicity. Therefore, in this review, the AI tools were investigated for the toxicity assessment of nanomaterials. To this end, a systematic search was performed on PubMed, Web of Science, and Scopus databases. Articles were included or excluded based on pre-defined inclusion and exclusion criteria, and duplicate studies were excluded. Finally, twenty-six studies were included. The majority of the studies were conducted on metal oxide and metallic nanoparticles. In addition, Random Forest (RF) and Support Vector Machine (SVM) had the most frequency in the included studies. Most of the models demonstrated acceptable performance. Overall, AI could provide a robust, fast, and low-cost tool for the evaluation of nanoparticle toxicity.

The Safe Coping Strategy of Nurses Working in the Care Units of Patients with COVID-19: A Qualitative Study.

Background: Nurses are in direct contact with patients with COVID-19 and have faced much tension with the rapid spread of coronavirus. This study aimed to explore the safe coping strategies of nurses when facing the COVID-19 pandemic. Materials and Methods: In this qualitative study, data were collected from September 20 to December 20, 2020, in Isfahan (Iran) through individual semi-structured interviews with 12 nurses working in the five referral centers for patients with COVID-19. Informants were selected via purposeful sampling and interviewed in one or several sessions at the appropriate time and place. The interviews continued until data saturation. All interviews continued until no new data were added to the continuous content analysis. Data analysis was performed using conventional content analysis based on Graneheim and Lundman's approach. We used Guba and Lincoln's criteria (including credibility, transferability, conformability, and dependability) to guarantee trustworthiness and rigor. Results: Safe coping strategies for nurses were discovered in two categories of "wise liberation" and "care," and six subcategories. "Wise liberation" consisted of four subcategories: "living in the moment," "accepting the inner and outer world," "life enrichment," and "building opportunities." "Care" contained two subcategories: "caring for others" and "caring for oneself." Conclusions: Discovering safe coping strategies for nurses could set the stage for special educational-therapeutic interventions so they can better understand their experiences and take advantage of the best coping strategies.

Organizational Climate of the COVID-19 Intensive Care Units: A Qualitative Content Analysis Study.

Introduction: To manage the psychological consequences of providing services in the COVID-19 intensive care units (ICUs), it is necessary to identify the experience of nurses from the organizational climate. The current study was conducted to explain the nurses' experience of the organizational climate of the COVID-19 ICUs. Methods: This qualitative study was conducted in three teaching hospitals affiliated to Isfahan University of Medical Sciences. 17 individual and semi-structured interviews with 12 nurses working in three selected COVID-19 centers were included in the data analysis. The participants were selected by purposive sampling and interviewed in one or more sessions at a suitable time and place. Interviews lasted for 45 to 90 minutes and continued with conventional content analysis until data saturation. Data analysis was done using conventional content analysis of Graham and Leideman model. Guba and Lincoln criteria (including validity, transferability, consistency, and reliability) were used to ensure reliability and accuracy. Results: The results of data analysis were classified into 82 primary concept codes and 10 sub-categories in the form of 3 categories: "positive climate of attachment and professional commitment", "emotional resonance in the work environment" and "supportive environment of the organization". Conclusion: This study led to the identification of nurses' experiences of the organizational climate during the COVID-19 which provides appropriate information to nursing managers to create a favorable organizational climate and increase the quality of work-life of nurses.

Prognosis of COVID-19 patients using lab tests: A data mining approach.

Background: The rapid prevalence of coronavirus disease 2019 (COVID-19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID-19 patients using data mining techniques. Methods: In this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other. Results: Based on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models. Conclusion: Data mining methods have the potential to be used for predicting outcomes of COVID-19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID-19 patients.

Synthesis and Radiolabeling of Glu-Urea-Lys with 99mTc-Tricarbonyl-Imidazole-Bathophenanthroline Disulfonate Chelation System and Biological Evaluation as Prostate-Specific Membrane Antigen Inhibitor.

Background: The Glu-Urea-Lys (EUK) pharmacophore as prostate-specific membrane antigen (PSMA)-targeted ligand was synthesized, radiolabeled with 99mTc-tricarbonyl-imidazole-BPS chelation system, and biological activities were evaluated. The strategy [2 + 1] ligand is applied for tricarbonyl labeling. (5-imidazole-1-yl)pentanoic acid as a monodentate ligand and bathophenanthroline disulfonate (BPS) as a bidentate ligand formed a chelate system with 99mTc-tricarbonyl. EUK-pentanoic acid-imidazole and EUK were evaluated for PSMA active site using AutoDock 4 software. Materials and Methods: EUK-pentanoic acid-imidazole was synthesized in two steps. BPS was radiolabeled with 99mTc-tricarbonyl at 100°C for 30 min. The purified 99mTc(CO)3(H2O)BPS was used to radiolabel EUK-pentanoic acid-imidazole at 100°C, 30 min. Radiochemical purity, Log P, and stability studies were carried out within 24 h. Affinity of 99mTc(CO)3BPS-imidazole-EUK was performed in the saturation binding studies using LNCaP cells at 37°C for 1 h with a range of 0.001-1000 nM radiolabeled compound range. Internalization studies were performed in LNCaP cells with 1000 nM radiolabeled compound incubated for (0-2) h at 37°C. Biodistribution was studied in normal male Balb/c mice. The artificial intelligence predicts the uptake of radiolabeled compound in tumor. Results: The structures of synthesized compounds were confirmed by mass spectroscopy. Radiochemical purity, Log P, and protein binding were ≥95%, -0.2%, and 23%, respectively. The radiolabeled compound was stable in saline and human plasma within 24 h with radiochemical purity ≥90%. There was no release of 99mTc within 4 h in competition with histidine. The affinity was 82 ± 26.38 nM, and the activity increased inside the cells over time. Biodistribution studies showed radioactivity accumulation in kidneys less than 99mTc-HYNIC-PSMA. There was a moderate accumulation of radioactivity in the liver and intestine. Conclusion: Based on the results, 99mTc(CO)3BPS-imidazole-EUK can potentially be used as an imaging agent for studies at prostate bed and distal areas. The chelate system can be potentially labeled with rhenium for imaging studies (fluorescent or scintigraphy) and therapy.

Novel biodegradable molecularly imprinted polymer nanoparticles for drug delivery of methotrexate anti-cancer; synthesis, characterization and cellular studies.

BACKGROUND: Recently biodegradable nanoparticles are the center of attention for the development of drug delivery systems. Molecularly imprinted polymer (MIP) is an interesting candidate for designing drug nano-carriers. MIP-based nanoparticles could be used for cancer treatment and exhibited the potential to fill gaps regarding to ligand-based nanomaterials. Also, the presence of a cross-linker can play an essential role in nanoparticle stability and physicochemical properties of nanoparticles after synthesis. OBJECTIVES: In this research, a biodegradable drug delivery system based on MIP nanoparticles was prepared using a biodegradable cross-linker (dimethacryloyl hydroxylamine, DMHA) for methotrexate (MTX). A hydrolysable functional group CO-O-NH-CO was added to the crosslinking agent to increase the final biodegradability of the polymer. METHODS: Firstly, a biodegradable cross-linker was synthesized. Then, the non-imprinted polymers were prepared through mini-emulsion polymerization in the absence of a template; and efficient particle size distribution was determined. Finally, methotrexate was placed in imprinted polymers to achieve the desired MIP. Different types of MIPs were synthesized using different molar ratios of template, cross-linker, and functional monomer, and the optimal molar ratio was obtained at 1:4:20, respectively. RESULTS: HNMR successfully confirmed the chemical structure of the cross-linker. According to SEM images, nanoparticles had a spherical shape with a smooth surface. The imprinted nanoparticles showed a narrow size distribution with an average of 120 nm at a high ratio of cross-linker. The drug loading and entrapment efficiency were 6.4% and 92%, respectively. The biodegradability studies indicated that the nanoparticles prepared by DMHA had a more degradability rate than ethylene glycol dimethacrylate as a conventional cross-linker. Also, the polymer degradation rate was higher in alkaline environments. Release studies in physiological and alkaline buffer showed an initial burst release of a quarter of loaded MTX during the day and a 70% release during a week. The Korsmeyer-Peppas model described the release pattern. The cytotoxicity of MTX loaded in nanoparticles was studied on the MCF-7 cell line, and the IC50 was 3.54 µg/ml. CONCLUSION: It was demonstrated that nanoparticles prepared by DMHA have the potential to be used as biodegradable drug carriers for anticancer delivery. Synthesis schema of molecular imprinting of methotrexate in biodegradable polymer based on dimethacryloyl hydroxylamine cross-linker, for use as nanocarrier anticancer delivery to breast tumor.

Correlation Between Fibroblast Growth Factor-23 and Pulmonary Arterial Hypertension in Hemodialysis Patients.

INTRODUCTION: Pulmonary artery hypertension (PAH) is common in end stage renal disease (ESRD) patients undergoing hemodialysis. Fibroblast growth factor-23 (FGF-23) increases in hemodialysis but its relationship with PAH is not completely understood. The aim of this study was to evaluate the relation between FGF-23 level and development of PAH in ESRD patients undergoing hemodialysis. METHODS: Patients undergoing hemodialysis for more than 6 months were enrolled in this cross-sectional study. Transthoracic echocardiography was performed to measure ejection fraction and pulmonary artery pressure (PAP) in all patients. Patients were grouped into normal PAP (PAP < 25 mmHg), elevated PAP (25 < PAP < 35 mmHg) and PAH (PAP > 35 mmHg). Parathormone hormone, calcium, phosphorus, vitamin D, and hemoglobin levels were also evaluated. RESULTS: Eighty-five patients (48 male, 56.47%) enrolled in this study. The mean age of the patients was 51.05 ± 16.45 years. Most of the patients (49, 57.65%) had normal PAP, 20 (23.53%) had elevated PAP and 16 (18.82%) had PAH. Serum biochemical markers and demographic characteristics were not significantly related to different PAP values (P > .05). Most of the patients (42, 49.41%) had normal FGF-23 levels. There was a significant relationship between PAP groups and FGF-23 and parathormone levels, P < .001, and P < .05; respectively. FGF-23 was significantly higher in PAH and elevated PAP groups compared with normal PAP group (P < .05). Only a significant positive correlation was observed between FGF-23 levels and PAP (P < .001). CONCLUSION: This finding highlights the possible role of FGF-23 in the development of vascular complications in ESRD patients.

Controllable Synthesis of Polymeric Micelles by Microfluidic Platforms for Biomedical Applications: A Systematic Review.

Polymeric micelles (PMs) are one of Nanoscale delivery systems with high stability, loading capacity, and biocompatibility. PMs are nano-sized and spherical particles with a hydrophilic shell and hydrophobic core or reverse depending on their applications. Polymeric micelles could be synthesized by different methods, such as direct dissolution, dialysis method, and lyophilization. Microfluidics is also a relatively modern approach for this purpose, in which chemical reactions are carried out in the microchannels. Compared with conventional preparation methods, the microfluidic technique produces homogeneous polymeric micelles with desirable features, tunable particle size, and relatively high drug loading. These advantages are originated from the ability of microfluidics in precise control over the streamlines of reactants without chaotic turbulence. Although the synthesis of polymeric micelles by the microfluidic platform is advantageous, little or no review has been conducted to provide a clear image of the different PMs preparation by the microfluidic approach. Thus, in this review, the production of the PMs, utilizing microfluidic procedures to enhance their favorable characteristics is investigated. For this purpose, an electronic search is conducted on PubMed, Web of Science, Scopus, and Embase databases for retrieval of relevant papers. Seven papers are included in this systematic review. Preparation of PMs by the microfluidic approach and the effect of different parameters, such as the flow rate ratio, channel dimensions, drug concentration, and organic solvent type on PMs characteristics is obtained from the included papers.

An investigation of affecting factors on MOF characteristics for biomedical applications: A systematic review.

Metal-organic frameworks (MOFs) are a fascinating class of crystalline porous materials composed of metal ions and organic ligands. Due to their attractive properties, MOFs can potentially offer biomedical field applications, such as drug delivery and imaging. This study aimed to systematically identify the affecting factors on the MOF characteristics and their effects on structural and biological characteristics. An electronic search was performed in four databases containing PubMed, Scopus, Web of Science, and Embase, using the relevant keywords. After analyzing the studies, 20 eligible studies were included in this review. As a result, various factors such as additives and organic ligand can influence the size and structure of MOFs. Additives are materials that can compete with ligand and may affect the nucleation and growth processes and, consequently, particle size. The nature and structure of ligand are influential in determining the size and structure of MOF. Moreover, synthesis parameters like the reaction time and initial reagents ratio are critical factors that should be optimized to regulate the size and structure. Of note is that the nature of the ligand and using a suitable additive can control the porosity of MOF. The more extended ligands aid in forming large pores. The choice of metallic nodes and organic ligand, and the MOF concentration are important factors since they can determine toxicity and biocompatibility of the final structure. The physicochemical properties of MOFs, such as hydrophobicity, affect the toxicity of nanoparticles. An increase in hydrophobicity causes increased toxicity of MOF. The biodegradability of MOF, as another property, depends on the organic ligand and metal ion and environmental conditions like pH. Photocleavable ligands can be served for controlled degradation of MOFs. Generally, by optimizing these affecting factors, MOFs with desirable properties will be obtained for biomedical applications.

Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study.

BACKGROUND: The recent global outbreak of coronavirus disease (COVID-19) is affecting many countries worldwide. Iran is one of the top 10 most affected countries. Search engines provide useful data from populations, and these data might be useful to analyze epidemics. Utilizing data mining methods on electronic resources' data might provide a better insight into the COVID-19 outbreak to manage the health crisis in each country and worldwide. OBJECTIVE: This study aimed to predict the incidence of COVID-19 in Iran. METHODS: Data were obtained from the Google Trends website. Linear regression and long short-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. All models were evaluated using 10-fold cross-validation, and root mean square error (RMSE) was used as the performance metric. RESULTS: The linear regression model predicted the incidence with an RMSE of 7.562 (SD 6.492). The most effective factors besides previous day incidence included the search frequency of handwashing, hand sanitizer, and antiseptic topics. The RMSE of the LSTM model was 27.187 (SD 20.705). CONCLUSIONS: Data mining algorithms can be employed to predict trends of outbreaks. This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly.