DeepBLS: Deep Feature-Based Broad Learning System for Tissue Phenotyping in Colorectal Cancer WSIs.

Tissue phenotyping is a fundamental step in computational pathology for the analysis of tumor micro-environment in whole slide images (WSIs). Automatic tissue phenotyping in whole slide images (WSIs) of colorectal cancer (CRC) assists pathologists in better cancer grading and prognostication. In this paper, we propose a novel algorithm for the identification of distinct tissue components in colon cancer histology images by blending a comprehensive learning system with deep features extraction in the current work. Firstly, we extracted the features from the pre-trained VGG19 network which are then transformed into mapped features space for nodes enhancement generation. Utilizing both mapped features and enhancement nodes, the proposed algorithm classifies seven distinct tissue components including stroma, tumor, complex stroma, necrotic, normal benign, lymphocytes, and smooth muscle. To validate our proposed model, the experiments are performed on two publicly available colorectal cancer histology datasets. We showcase that our approach achieves a remarkable performance boost surpassing existing state-of-the-art methods by (1.3% AvTP, 2% F1) and (7% AvTP, 6% F1) on CRCD-1, and CRCD-2, respectively.

Improvement of the Self-Controlled Hyperthermia Applications by Varying Gadolinium Doping in Lanthanum Strontium Manganite Nanoparticles.

In this study, silica-encapsulated gadolinium was doped in lanthanum strontium manganite nanoparticles (NPs) with different concentrations using the citrate-gel auto-combustion method. We focused on tuning the Curie temperature and enhancing the specific absorption rate (SAR) of silica-coated gadolinium-doped lanthanum strontium manganite NPs to make them suitable for self-controlled magnetic hyperthermia. The samples were characterized by using transmission electron microscopy (TEM), X-ray diffraction, Fourier transform infrared spectroscopy (FTIR), and magnetic measurements to examine the structural, optical, and magnetic properties of the manganite NPs. While our results exhibit a successful doping of gadolinium in lanthanum strontium manganite NPs, we further prepared magnetic core NPs with sizes between 20 and 50 nm. The Curie temperature of the NPs declined with increasing gadolinium doping, making them promising materials for hyperthermia applications. The Curie temperature was measured using the magnetization (M-T) curve. Magnetic heating was carried out in an external applied AC magnetic field. Our present work proved the availability of regulating the Curie temperature of gadolinium-doped lanthanum strontium manganite NPs, which makes them promising candidates for self-controlled magnetic hyperthermia applications.

Epidemiology of premalignant and malignant upper gastrointestinal lesions among patients presenting from a rural community in Karachi, Pakistan.

Objective: Understanding the epidemiology of upper gastrointestinal cancers in Pakistan may help in identifying important demographic risk factors for upper gastrointestinal malignancies in a particular rural population group. This will benefit in implementing tailored prevention approaches as well as effective management of health services. Method: A secondary data analysis of 1193 patients was conducted who went through diagnostic upper GI endoscopy between December 2016 to May 2019 at Fatima Hospital. The endoscopies were performed at Fatima Hospital which is the main health resource for the specifically targeted rural community. Data was analyzed using SPSS version 21. Results: The median age of patients included in the sample was 35 years (IQR=20 years). One third of all endoscopic findings were concluded as normal. The frequency of malignant upper gastrointestinal lesions was relatively higher among male and patients with age 65 years or more. The study didn't find any significant differences in the distribution of malignancies on the basis of ethnicity. Adenocarcinoma of esophagus was the most common malignant lesion. Conclusion: The average age of patients undergoing upper gastrointestinal endoscopy among rural community of Karachi was relatively low. The burden of upper GI malignancies was significantly higher among elderly. Male patients had significantly greater burden of premalignant and malignant lesions as compared to females. No differences in the distribution of diagnostic outcomes were observed on the basis of ethnicity.

Efficacy of hypertonic saline versus isotonic saline among children with cystic fibrosis: A systematic review and meta-analysis.

Background: Inhaled hypertonic saline (HS) reduces pulmonary exacerbations in patients with cystic fibrosis (CF) aged 6 or more years. However, the effectiveness of HS in improving clinical outcomes in younger children aged 6 or less years is not established. This study examines the efficacy of HS in younger CF patients. Methods: Searches were conducted across three databases (Medline, Cochrane Central and EMBASE) from inception through July 2022. Randomized controlled trials assessing the impact of HS in younger CF patients were included. Trials involving only patients greater than 6 years or control group other than isotonic saline (IS) were excluded. Outcomes measured included lung clearance index (LCI), cystic fibrosis questionnaire (CFQ-R) score, spirometry measures, oxygen saturation, respiratory rate, height and weight. Outcomes were reported as mean differences (MDs) with 95% confidence intervals. Results: Seven studies (n = 390 patients) were included in this review. HS significantly reduced the LCI (MD: -0.67; 95%CI, -1.05 to 0.29, P = 0.0006) compared to IS. In addition, HS was associated with significant improvements in height (MD: 2.23; 95%CI, -0.00 to 4.46, P = 0.05) and CFQ-R (MD: 4.30; 95%CI, 0.65-7.95, P = 0.02), but not in oxygen saturation (MD: -0.15; 95%CI, -0.54 to 0.25, P = 0.47), respiratory rate (MD: -0.21; 95%CI, -2.19 to 1.77, P = 0.83) or weight (MD: 0.70; 95%CI, -0.47 to 1.87, P = 0.24). Furthermore, HS did not significantly improve spirometry measures, including FEV1 (MD: -0.11; 95%CI, -0.21 to 0.43, P = 0.51) and forced vital capacity (MD: 0.27; 95%CI, -0.49 to 1.04, P = 0.48), but significantly improved FEF25-75 (MD: 0.12; 95% CI, 0.05-0.20; P = 0.002). Discussion: Treatment with HS in younger children with CF improves lung clearance, symptoms and quality of life. FEF25-75 may prove a more sensitive measure for assessing intervention related improvements in pediatric CF trials. Conclusion: The findings support HS as a therapeutic method in CF-affected children.

Transfer learning for genotype-phenotype prediction using deep learning models.

BACKGROUND: For some understudied populations, genotype data is minimal for genotype-phenotype prediction. However, we can use the data of some other large populations to learn about the disease-causing SNPs and use that knowledge for the genotype-phenotype prediction of small populations. This manuscript illustrated that transfer learning is applicable for genotype data and genotype-phenotype prediction. RESULTS: Using HAPGEN2 and PhenotypeSimulator, we generated eight phenotypes for 500 cases/500 controls (CEU, large population) and 100 cases/100 controls (YRI, small populations). We considered 5 (4 phenotypes) and 10 (4 phenotypes) different risk SNPs for each phenotype to evaluate the proposed method. The improved accuracy with transfer learning for eight different phenotypes was between 2 and 14.2 percent. The two-tailed p-value between the classification accuracies for all phenotypes without transfer learning and with transfer learning was 0.0306 for five risk SNPs phenotypes and 0.0478 for ten risk SNPs phenotypes. CONCLUSION: The proposed pipeline is used to transfer knowledge for the case/control classification of the small population. In addition, we argue that this method can also be used in the realm of endangered species and personalized medicine. If the large population data is extensive compared to small population data, expect transfer learning results to improve significantly. We show that Transfer learning is capable to create powerful models for genotype-phenotype predictions in large, well-studied populations and fine-tune these models to populations were data is sparse.

Unique design approach to realize an O-band laser monolithically integrated on 300†mm Si substrate by nano-ridge engineering.

We introduce a new design space for optimizing III-V devices monolithically grown on Silicon substrates by extending the concept of nano-ridge engineering from binary semiconductors such as GaAs, InAs and GaSb to the ternary alloy InGaAs. This allows controlling the fundamental lattice constant of the fully relaxed ternary nano-ridge which thereby serves as a tunable base for the integration of diverse device hetero-layers. To demonstrate the flexibility of this approach, we realized an O-band nano-ridge laser containing three In0.45Ga0.55As quantum wells, which are pseudomorphically strained to an In0.25Ga0.75As nano-ridge base. The demonstration of an optically pumped nano-ridge laser operating around 1300 nm underlines the potential of this cost-efficient and highly scalable integration approach for silicon photonics.

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods.

BACKGROUND: Genotype-phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variations in human beings. There are many approaches for finding the association which can be broadly categorized into two classes, statistical techniques, and machine learning. Statistical techniques are good for finding the actual SNPs causing variation where Machine Learning techniques are good where we just want to classify the people into different categories. In this article, we examined the Eye-color and Type-2 diabetes phenotype. The proposed technique is a hybrid approach consisting of some parts from statistical techniques and remaining from Machine learning. RESULTS: The main dataset for Eye-color phenotype consists of 806 people. 404 people have Blue-Green eyes where 402 people have Brown eyes. After preprocessing we generated 8 different datasets, containing different numbers of SNPs, using the mutation difference and thresholding at individual SNP. We calculated three types of mutation at each SNP no mutation, partial mutation, and full mutation. After that data is transformed for machine learning algorithms. We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96% respectively. Stacked ensembles of LSTM outperformed other algorithms for 1560 SNPs with an overall accuracy of 0.96, AUC = 0.98 for brown eyes, and AUC = 0.97 for Blue-Green eyes. The main dataset for Type-2 diabetes consists of 107 people where 30 people are classified as cases and 74 people as controls. We used different linear threshold to find the optimal number of SNPs for classification. The final model gave an accuracy of 0.97%. CONCLUSION: Genotype-phenotype predictions are very useful especially in forensic. These predictions can help to identify SNP variant association with traits and diseases. Given more datasets, machine learning model predictions can be increased. Moreover, the non-linearity in the Machine learning model and the combination of SNPs Mutations while training the model increases the prediction. We considered binary classification problems but the proposed approach can be extended to multi-class classification.

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM.

COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.

Integration of high-speed GaAs metal-semiconductor-metal photodetectors by means of transfer printing for 850 nm wavelength photonic interposers.

We propose and demonstrate the integration of 850 nm GaAs-based metal-semiconductor-metal (MSM) photodetectors (PDs) based on transfer printing for application in photonic interposers. Both devices that directly interface with a multimode optical fiber (with device dimensions of 70 µm × 70 µm) as well as devices that interface with a SiN waveguide layer through a grating coupler (with device dimensions of 30 µm × 30 µm) are demonstrated. The dark currents are measured to be 22 nA and 7.2 nA at 2 V bias for the larger and smaller PDs respectively. For 850 nm wavelength, the external responsivities are measured to be 0.117 A/W and 0.1 A/W at 2 V bias. 20 GHz bandwidth is measured. Open 40 Gb/s eye diagrams are realized.

III-V-on-Silicon Photonic Integrated Circuits for Spectroscopic Sensing in the 2-4 µm Wavelength Range.

The availability of silicon photonic integrated circuits (ICs) in the 2-4 µm wavelength range enables miniature optical sensors for trace gas and bio-molecule detection. In this paper, we review our recent work on III-V-on-silicon waveguide circuits for spectroscopic sensing in this wavelength range. We first present results on the heterogeneous integration of 2.3 µm wavelength III-V laser sources and photodetectors on silicon photonic ICs for fully integrated optical sensors. Then a compact 2 µm wavelength widely tunable external cavity laser using a silicon photonic IC for the wavelength selective feedback is shown. High-performance silicon arrayed waveguide grating spectrometers are also presented. Further we show an on-chip photothermal transducer using a suspended silicon-on-insulator microring resonator used for mid-infrared photothermal spectroscopy.

III-V-on-silicon 2-µm-wavelength-range wavelength demultiplexers with heterogeneously integrated InP-based type-II photodetectors.

2-µm-wavelength-range silicon-on-insulator (SOI) arrayed waveguide gratings (AWGs) with heterogeneously integrated InP-based type-II quantum well photodetectors are presented. Low insertion loss (2.5-3 dB) and low crosstalk (-30 to -25 dB) AWGs are realized. The InP-based type-II photodetectors are integrated with the AWGs using two different coupling approaches. Adiabatic-taper-based photodetectors show a responsivity of 1.6 A/W at 2.35 µm wavelength and dark current of 10 nA at -0.5 V, while photodetectors using grating-assisted coupling have a responsivity of 0.1 A/W and dark current of 5 nA at -0.5 V. The integration of the photodetector array does not degrade the insertion loss and crosstalk of the device. The photodetector epitaxial stack can also be used to realize the integration of a broadband light source, thereby enabling fully integrated spectroscopic systems.

2.3 µm range InP-based type-II quantum well Fabry-Perot lasers heterogeneously integrated on a silicon photonic integrated circuit.

Heterogeneously integrated InP-based type-II quantum well Fabry-Perot lasers on a silicon waveguide circuit emitting in the 2.3 µm wavelength range are demonstrated. The devices consist of a "W"-shaped InGaAs/GaAsSb multi-quantum-well gain section, III-V/silicon spot size converters and two silicon Bragg grating reflectors to form the laser cavity. In continuous-wave (CW) operation, we obtain a threshold current density of 2.7 kA/cm2 and output power of 1.3 mW at 5 °C for 2.35 µm lasers. The lasers emit over 3.7 mW of peak power with a threshold current density of 1.6 kA/cm2 in pulsed regime at room temperature. This demonstration of heterogeneously integrated lasers indicates that the material system and heterogeneous integration method are promising to realize fully integrated III-V/silicon photonics spectroscopic sensors in the 2 µm wavelength range.

2 µm wavelength range InP-based type-II quantum well photodiodes heterogeneously integrated on silicon photonic integrated circuits.

The heterogeneous integration of InP-based type-II quantum well photodiodes on silicon photonic integrated circuits for the 2 µm wavelength range is presented. A responsivity of 1.2 A/W at a wavelength of 2.32 µm and 0.6 A/W at 2.4 µm wavelength is demonstrated. The photodiodes have a dark current of 12 nA at -0.5 V at room temperature. The absorbing active region of the integrated photodiodes consists of six periods of a "W"-shaped quantum well, also allowing for laser integration on the same platform.

Ge-on-Si and Ge-on-SOI thermo-optic phase shifters for the mid-infrared.

Germanium-on-silicon thermo-optic phase shifters are demonstrated in the 5 µm wavelength range. Basic phase shifters require 700 mW of power for a 2π phase shift. The required power is brought down to 80 mW by complete undercut using focused ion beam. Finally an efficient thermo-optic phase shifter is demonstrated on the germanium on SOI platform. A tuning power (for a 2π phase shift) of 105 mW is achieved for a Ge-on-SOI structure which is lowered to 16 mW for a free standing phase shifter.

Impact of dental caries on the daily lives of geriatric patients visiting dental hospitals in Rawalpindi, Pakistan.

Background: The objectives were to assess the impact of dental caries on the daily living of the geriatric population and determine the factors that influence the relationship between dental health and the daily living of the geriatric population. Methods: A descriptive cross-sectional study was carried out over six months at Rawalpindi's public and private dental hospitals. Participants aged≥60 years, both male and female, were selected. The calculated sample size was 281. Desired sample from one of the dental hospitals was collected using a non-probability consecutive sampling strategy. Data about sociodemographic characteristics and the DMFT index were collected. Adapted validated tool dental impact on daily living (DIDL) was used to assess the impact of dental health on daily living. Results: Chi-squared test of association showed a positive association between the DIDL and sociodemographic variables, including age (P=0.001), gender (P=0.001), education (P=0.001), income (P=0.001), occupation (P=0.029), marital status (P=0.001), living arrangement (P=0.001), and history of chronic illnesses (P=0.001). The association between the DMFT index and DIDL also showed statistically significant results (P=0.001). Binary logistic regression analysis indicated that gender (OR=6.98, P=0.005) and the individual's dental health (OR=6.43, P=0.001) were the strongest predictors of the impact experienced in daily life activities. The overall model was statistically significant (χ2=51.24, P=0.001), and the variables were responsible for 32.4% of the variance in the outcome variable. Conclusion: The study provides strong evidence that sociodemographic factors, DMFT index, gender, and individual dental health significantly contribute to the impact of dental health on daily living. Gender and individual dental health emerge as particularly influential predictors. These findings emphasize the need for targeted interventions and awareness programs, especially for groups with a higher risk of experiencing a significant impact on daily life due to dental issues.

Proximal pulmonary vein stenosis detection in pediatric patients: value of multiplanar and 3-D VR imaging evaluation.

BACKGROUND: One of the important benefits of using multidetector computed tomography (MDCT) is its capability to generate high-quality two-dimensional (2-D) multiplanar (MPR) and three-dimensional (3-D) images from volumetric and isotropic axial CT data. However, to the best of our knowledge, no results have been published on the potential diagnostic role of multiplanar and 3-D volume-rendered (VR) images in detecting pulmonary vein stenosis, a condition in which MDCT has recently assumed a role as the initial noninvasive imaging modality of choice. OBJECTIVE: The purpose of this study was to compare diagnostic accuracy and interpretation time of axial, multiplanar and 3-D VR images for detection of proximal pulmonary vein stenosis in children, and to assess the potential added diagnostic value of multiplanar and 3-D VR images. MATERIALS AND METHODS: We used our hospital information system to identify all consecutive children (< 18 years of age) with proximal pulmonary vein stenosis who had both a thoracic MDCT angiography study and a catheter-based conventional angiography within 2 months from June 2005 to February 2012. Two experienced pediatric radiologists independently reviewed each MDCT study for the presence of proximal pulmonary vein stenosis defined as ≥ 50% of luminal narrowing on axial, multiplanar and 3-D VR images. Final diagnosis was confirmed by angiographic findings. Diagnostic accuracy was compared using the z-test. Confidence level of diagnosis (scale 1-5, 5 = highest), perceived added diagnostic value (scale 1-5, 5 = highest), and interpretation time of multiplanar or 3-D VR images were compared using paired t-tests. Interobserver agreement was measured using the chance-corrected kappa coefficient. RESULTS: The final study population consisted of 28 children (15 boys and 13 girls; mean age: 5.2 months). Diagnostic accuracy based on 116 individual pulmonary veins for detection of proximal pulmonary vein stenosis was 72.4% (84 of 116) for axial MDCT images, 77.5% (90 of 116 cases) for multiplanar MDCT images, and 93% (108 of 116 cases) for 3-D VR images with significantly higher accuracy with 3-D VR compared to axial (z = 4.17, P < 0.001) and multiplanar (z = 3.34, P < 0.001) images. Confidence levels for detection of proximal pulmonary vein stenosis were significantly higher with 3-D VR images (mean level: 4.6) compared to axial MDCT images (mean level: 1.7) and multiplanar MDCT images (mean level: 2.0) (paired t-tests, P < 0.001). Thus, 3-D VR images (mean added diagnostic value: 4.7) were found to provide added diagnostic value for detecting proximal pulmonary vein stenosis (paired t-test, P < 0.001); however, multiplanar MDCT images did not provide added value (paired t-test, P = 0.89). Interpretation time was significantly longer and interobserver agreement was higher when using 3-D VR images than using axial MDCT images or MPR MDCT images for diagnosing proximal pulmonary vein stenosis (paired t-tests, P < 0.001). CONCLUSIONS: Use of 3-D VR images in the diagnosis of proximal pulmonary vein stenosis in children significantly increases accuracy, confidence level, added diagnostic value and interobserver agreement. Thus, the routine use of this technique should be encouraged despite its increased interpretation time.

Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU.

Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU.

Assessment of the Dimensions of Coronary Arteries for the Manifestation of Coronary Artery Disease.

INTRODUCTION: The size of the coronary artery influences the effective outcome of therapeutic measures like coronary artery bypass graft (CABG) surgery, percutaneous coronary interventions (PCI), and diagnosis of coronary artery disease. Patients' age, gender, BMI, anatomical variations, and increased left ventricular size all have an effect on coronary artery parameters. OBJECTIVE: This study aims to compare the average size of the coronary arteries of the Pakistani population in both sexes for manifestation of coronary artery disease. METHODOLOGY: For the analysis of the coronary arteries, 100 patients of both sexes, male and female, were taken. X-ray angiography was performed for two-dimensional images of coronary arteries. For diameter measurement, images were visualized on quantitative coronary angiography (QCA) in different views (caudal and cranial views). The diameters of the left main coronary artery (left main stem/LMS), left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA) were measured on angiograms. Data about the dimensions of the coronary artery was gathered through quantitative angiography. Data analysis was done through SPSS version 26 (IBM Corp., Armonk, NY). RESULTS:  There is a notable distinction in the average diameters among the proximal LAD (3.12), mid-LAD (2.40), and distal LAD (1.29). A statistically significant difference is evident among mid-LCx, distal LCx, and proximal LCx (p-value < 0.001). Likewise, the average diameter of the distal RCA (1.89) was smaller when compared to the mid-RCA (3.19) and proximal RCA (3.78). However, there was no significant difference in the average diameter among mid-LMS, distal LMS, and proximal LMS (p-value = 0.09). CONCLUSION: The average diameter of distal RCA was smaller when compared to mid-RCA and proximal RCA. The average size of proximal LAD and proximal LCx was comparatively larger than mid- and distal LAD and LCx. The findings of current research will be beneficial for the diagnosis and management of coronary artery disease patients.

Ventilator associated pneumonia in intensive care unit patients: a systematic review.

Ventilator-associated pneumonia (VAP) is the most common ICU acquired pneumonia among patients who are invasively intubated for mechanical ventilation. Patients with VAP suffer an increased mortality risk, financial burden, and length of stay in the hospital. The authors aimed to review the literature to describe the incidence, mortality, and microbiological evidence of VAP. We selected 13 peer-reviewed articles published from 1 January 2010 to 15 September 2022 from electronic databases for studies among adult or pediatric patients diagnosed with VAP expressed per thousand days admitted in the ICU. The VAP rates ranged from 7 to 43 per thousand days, varying among different countries of the world. A significant rate of mortality was observed in 13 studies ranging from 6.3 to 66.9%. Gram-negative organisms like Acinetobacter spp., Pseudomonas aeruginosa Gram-positive organisms like Staphylococcus aureus were frequently found. Our findings suggest an alarming situation of VAP among patients admitted to the intensive care units with increasing incidence and mortality. The review also found that VAP is more common in males and that there is a significant variation in the incidence and mortality rates of VAP among different countries. The findings of this review can inform the development of infection control and prevention strategies to reduce the burden of VAP. Thus, there is a crucial need for control and preventive measures like interventional studies and educational programs on staff training, hand-hygiene, and the appropriate use of ventilator bundle approach to curb this preventable threat that is increasing at an alarming rate.