Nutrition and Dietary Intervention in Cancer: Gaps, Challenges, and Future Perspectives.

The term "cancer" refers to the state in which cells in the body develop mutations and lose control over their replication. Malignant cancerous cells invade in various other tissue sites of the body. Chemotherapy, radiation, and surgery are the first-line modalities for the majority of solid cancers. These treatments work by mitigating the DNA damage of cancerous cells, but they can also cause harm to healthy cells. These side effects might be immediate or delayed, and they can cause a high rate of morbidity and mortality. Dietary interventions have a profound impact on whole-body metabolism, including immunometabolism and oncometabolism which have been shown to reduce cancer growth, progression, and metastasis in many different solid tumor models with promising outcomes in early phase clinical studies. Dietary interventions can improve oncologic or quality-of-life outcomes for patients that are undergoing chemotherapy or radiotherapy. In this chapter, we will focus on the impact of nutritional deficiencies, several dietary interventions and their proposed mechanisms which are used as a novel therapy in controlling and managing cancers.

Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database.

Currently, the coronavirus disease 2019 (COVID19) pandemic has killed more than one million people worldwide. In the present outbreak, radiological imaging modalities such as computed tomography (CT) and X-rays are being used to diagnose this disease, particularly in the early stage. However, the assessment of radiographic images includes a subjective evaluation that is time-consuming and requires substantial clinical skills. Nevertheless, the recent evolution in artificial intelligence (AI) has further strengthened the ability of computer-aided diagnosis tools and supported medical professionals in making effective diagnostic decisions. Therefore, in this study, the strength of various AI algorithms was analyzed to diagnose COVID19 infection from large-scale radiographic datasets. Based on this analysis, a light-weighted deep network is proposed, which is the first ensemble design (based on MobileNet, ShuffleNet, and FCNet) in medical domain (particularly for COVID19 diagnosis) that encompasses the reduced number of trainable parameters (a total of 3.16 million parameters) and outperforms the various existing models. Moreover, the addition of a multilevel activation visualization layer in the proposed network further visualizes the lesion patterns as multilevel class activation maps (ML-CAMs) along with the diagnostic result (either COVID19 positive or negative). Such additional output as ML-CAMs provides a visual insight of the computer decision and may assist radiologists in validating it, particularly in uncertain situations Additionally, a novel hierarchical training procedure was adopted to perform the training of the proposed network. It proceeds the network training by the adaptive number of epochs based on the validation dataset rather than using the fixed number of epochs. The quantitative results show the better performance of the proposed training method over the conventional end-to-end training procedure. A large collection of CT-scan and X-ray datasets (based on six publicly available datasets) was used to evaluate the performance of the proposed model and other baseline methods. The experimental results of the proposed network exhibit a promising performance in terms of diagnostic decision. An average F1 score (F1) of 94.60% and 95.94% and area under the curve (AUC) of 97.50% and 97.99% are achieved for the CT-scan and X-ray datasets, respectively. Finally, the detailed comparative analysis reveals that the proposed model outperforms the various state-of-the-art methods in terms of both quantitative and computational performance.

Efficacy of Whole-Blood Del Nido Cardioplegia Compared with Diluted Del Nido Cardioplegia in Coronary Artery Bypass Grafting: A Retrospective Monocentric Analysis of Pakistan.

Background and Objectives: Cardioplegia is one of the most significant components used to protect the myocardium during cardiac surgery. There is a paucity of evidence regarding the utilization of whole-blood Del Nido cardioplegia (WB-DNC) on clinical outcomes in coronary artery bypass grafting (CABG). The purpose of this retrospective cross-sectional study is to compare the effectiveness of diluted (blood to crystalloid; 1:4) Del Nido cardioplegia (DNC) with WB-DNC in patients who underwent elective CABG in a tertiary care hospital in Lahore-Pakistan. Materials and Methods: This was a retrospective descriptive study conducted at the Department of Cardiovascular Surgery, King Edward Medical University, Lahore. The medical database of all consecutive patients admitted from January 2018 to March 2020 and who fulfilled the inclusion criteria were reviewed. Results: Out of 471 patients admitted during the study period, 450 underwent various elective cardiac surgeries. Out of 450, 321 patients (71.33%) were operated on for CABG. Only 234/321 (72.89%) CABG patients fulfilled our inclusion criteria; 120 (51.28%) patients received WB-DNC, while 114 (48.71%) patients were administered with DNC. The former group presented with better clinical outcomes compared with the latter in terms of lesser requirements of inotropic support, low degree of hemodilution, shorter in-hospital stay, improved renal function, and cost-effectiveness. Peak values of serum Troponin-T (Trop-T), creatine kinase-myocardial band (CK-MB) release, and activated clotting time (ACT) were also lower in the WB-DNC group compared with the DNC group. Conclusions: The WB-DNC conferred better myocardial protection, improved early clinical outcomes, and also proved to be economical for patients undergoing elective CABG compared with classical crystalloid cardioplegia solution.

Identification of potential inhibitors of Zika virus NS5 RNA-dependent RNA polymerase through virtual screening and molecular dynamic simulations.

Zika virus (ZIKV) is one of the mosquito borne flavivirus with several outbreaks in past few years in tropical and subtropical regions. The non-structural proteins of flaviviruses are suitable active targets for inhibitory drugs due to their role in pathogenicity. In ZIKV, the non-structural protein 5 (NS5) RNA-Dependent RNA polymerase replicates its genome. Here we have performed virtual screening to identify suitable ligands that can potentially halt the ZIKV NS5 RNA dependent RNA polymerase (RdRp). During this process, we searched and screened a library of ligands against ZIKV NS5 RdRp. The selected ligands with significant binding energy and ligand-receptor interactions were further processed. Among the selected docked conformations, top five was further optimized at atomic level using molecular dynamic simulations followed by binding free energy calculations. The interactions of ligands with the target structure of ZIKV RdRp revealed that they form strong bonds within the active sites of the receptor molecule. The efficacy of these drugs against ZIKV can be further analyzed through in-vitro and in-vivo studies.

Photoinduced development of antibacterial materials derived from isosorbide moiety.

A straightforward method for immobilizing in situ generated silver nanoparticles on the surface of a photoactivable isosorbide-derived monomer is developed with the objective to design a functional material having antibacterial properties. The photoinduced thiol-ene mechanism involved in these syntheses is described by the electron spin resonance/spin trapping technique. The resulting materials with or without silver nanoparticles (Ag NPs) were used as films or as coatings on glass substrate. The surface of the synthesized materials was characterized by X-ray photoelectron spectroscopy and scanning electron microscopy, and their thermal and mechanical properties were evaluated by dynamic-mechanical thermal tests, differential scanning calorimetry, thermogravimetric analyses, along with pencil hardness, nanoindentation, and scratch resistance tests. The photoinduced formation of Ag NPs is also confirmed by UV spectrophotometry. Finally, a primary investigation demonstrates the antibacterial properties of the isosorbide-derived material against Staphylococcus aureus and Escherichia coli, as well as its cytocompatibility toward NIH 3T3 fibroblastic cells.

The Right Hand Must Know What the Left Hand is Doing: A False-Positive Hotspot on the Sestamibi Scan.

Objective: Imaging studies in the setting of primary hyperparathyroidism are performed to rule out an ectopic parathyroid adenoma. Although rare, false-positive scans can cause confusion and possibly more extensive procedures. Method: A 68-year-old woman with parathyroid hormone-dependent hypercalcemia was found to have uptake in the left midclavicular area on the parathyroid scan with sestamibi. Retention of the isotope was considered a possibility, and the sestamibi scan was repeated after injecting the isotope in the right hand and this did not show uptake in the left midclavicular area. Results: Sestamibi is taken up by the mitochondrial-rich adenoma cells and can help identify an ectopic location of the adenoma. Sestamibi scans are commonly performed before neck exploration to rule out an ectopic adenoma and to localize the parathyroid adenoma. Thyroid adenoma and thyroid cancer can also cause retention of isotopes. Retention of the isotope in the vein can also give an illusion of an ectopic parathyroid adenoma. Injecting the isotope in the contralateral hand can overcome this retention issue. Conclusion: Uptake on parathyroid scan outside of normal embryologic decent of the parathyroid gland should raise the possibility of a false-positive uptake.

Two Cases of Symptomatic Familial Hypocalciuric Hypercalcemia: Treatment Response to Calcimimetic Therapy.

Familial hypocalciuric hypercalcemia (FHH) is marked by mild to moderate hypercalcemia, normal-elevated serum PTH levels, and relative hypocalciuria. Cinacalcet, a calcimimetic therapy, has been reported to reduce symptom burden and serum calcium levels in FHH. We report 2 adult males with chronic hypercalcemia, with initial concerns for primary hyperparathyroidism. Urine calcium screening and genetic testing confirmed FHH in both patients. Shortened QTc normalized while on cinacalcet in the first patient and reductions in serum calcium and PTH levels without symptomatic hypercalcemia were noted in the second patient. Calcimimetic therapy can potentially be offered to FHH patients, particularly those with hypercalcemia symptoms, serum calcium levels >1 mg/dL (0.25 mmol/L) above normal or at risk of cardiac arrhythmias. Cinacalcet treatment was overall well tolerated and significantly reduced serum calcium and PTH levels in 2 adult FHH patients over time. Calcimimetic therapy has shown promise in managing persistent hypercalcemia and potential adverse events in FHH patients. Potential barriers include indefinite treatment, cost, and possible adverse effects.

Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective.

The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.

Aloesin-loaded chitosan/cellulose-based scaffold promotes skin tissue regeneration.

Skin wound healing and regeneration is very challenging across the world as simple or acute wounds can be transformed into chronic wounds or ulcers due to foreign body invasion, or diseases like diabetes or cancer. The study was designed to develop a novel bioactive scaffold, by loading aloesin to chitosan-coated cellulose scaffold, to cure full-thickness skin wounds. The physiochemical characterization of the scaffold was carried out using scanning electron microscopy (SEM) facilitated by energy-dispersive spectrophotometer (EDS), atomic force microscopy (AFM), and Fourier transform infrared spectroscopy (FTIR). The results indicated the successful coating of chitosan and aloesin on cellulose without any physical damage. The drug release kinetics confirmed the sustained release of aloesin by showing a cumulative release of up to 88 % over 24 h. The biocompatibility of the aloesin-loaded chitosan/cellulose (AlCsCFp) scaffold was evaluated by the WST-8 assay that confirmed the significantly increased adherence and proliferation of fibroblasts on the AlCsCFp scaffold. The in vivo wound healing study showed that both 0.05 % and 0.025 % AlCsCFp scaffolds have significantly higher wound closure rates (i.e. 88.2 % and 95.6 % approximately) as compared to other groups. This showed that novel composite scaffold has a wound healing ability. Furthermore, histological and gene expression analysis demonstrated that the scaffold also induced cell migration, angiogenesis, re-epithelialization, collagen deposition, and tissue granulation formation. Thus, it is concluded that the aloesin-loaded chitosan/cellulose-based scaffold has great therapeutic potential for being used in wound healing applications in the clinical setting in the future.

Chitosan as a tool for tissue engineering and rehabilitation: Recent developments and future perspectives - A review.

Chitosan has established itself as a multifunctional and auspicious biomaterial within the domain of tissue engineering, presenting a decade of uninterrupted advancements and novel implementations. This article provides a comprehensive overview of the most recent developments in chitosan-based tissue engineering, focusing on significant progress made in the last ten years. An exploration is conducted of the various techniques utilized in the modification of chitosan and the production of scaffolds, with an analysis of their effects on cellular reactions and tissue regeneration. The investigation focuses on the integration of chitosan with other biomaterials and the addition of bioactive agents to improve their functionalities. Upon careful analysis of the in vitro and in vivo research, it becomes evident that chitosan effectively stimulates cell adhesion, proliferation, and differentiation. Furthermore, we offer valuable perspectives on the dynamic realm of chitosan-based approaches tailored to distinct tissue categories, including nerve, bone, cartilage, and skin. The review concludes with a discussion of prospective developments, with particular attention given to possible directions for additional study, translational implementations, and the utilization of chitosan to tackle existing obstacles in the field of tissue engineering. This extensive examination provides a significant amalgamation of the advancements achieved over the previous decade and directs scholars towards uncharted territories in chitosan-based tissue engineering.

Pseudo Primary Aldosteronism as Initial Presentation of Ectopic ACTH Syndrome in Metastatic Small Cell Lung Cancer.

Ectopic adrenocorticotropic hormone (ACTH)-secreting syndrome (EAS) is a rare but often aggressive paraneoplastic syndrome. Patients with EAS typically present with high ACTH levels and rapid clinical progression in the setting of acute cortisol elevation, which can delay diagnosis due to a lack of typical Cushingoid features. High levels of ACTH have also been shown to stimulate the adrenal zona glomerulosa to oversecrete aldosterone. We present the case of a 58-year-old male individual presenting with new-onset hypertension and severe metabolic alkalosis with spontaneous hypokalemia, in the setting of elevated aldosterone and low renin levels, suggestive of primary aldosteronism. Subsequent biochemical testing, imaging, and pathology, however, revealed suppression of aldosterone with evidence of hypercortisolism in the setting of metastatic small cell lung cancer. This was, therefore, suggestive of pseudo primary aldosteronism in the setting of a paraneoplastic ectopic ACTH-producing syndrome. This case highlights that hypercortisolism, in the setting of EAS, can initially present with a clinical picture suggestive of hyperaldosteronism. The use of a dexamethasone suppression test can allow the clinician to differentiate between idiopathic bilateral adrenal hyperplasia and ectopic ACTH syndrome.

Pellagra Post-Roux-en-Y Gastric Bypass Surgery.

Background/Objective: Micronutrient deficiencies such as pellagra are rarely seen after bariatric surgery and can be challenging to diagnose and manage. Alcohol use can precipitate nutritional deficiencies. Case Report: A 51-year-old woman with a history of Roux-en-Y gastric bypass surgery who later developed an alcohol-use disorder after her diagnosis of breast cancer. She experienced a subacute decline in her physical and cognitive function along with a rash after radiation treatment for breast cancer, lower extremity pain and weakness, anemia, and diarrhea with severe hypokalemia. Workup showed undetectable niacin levels. She initially did not respond to an oral niacin replacement, necessitating intramuscular injections. Alcohol cessation and parenteral B complex replacement led to the resolution of her symptoms and biochemical derangements. Discussion: Bariatric surgery with concomitant alcohol use can precipitate niacin deficiency-induced liver dysfunction. In the correct clinical setting, screening for alcohol use and checking niacin levels may help avoid extensive testing and can help make the correct diagnosis. Parenteral replacement may be necessary in this setting. Conclusion: Niacin deficiency needs to be considered in patients with bariatric surgery with a history of alcoholism in the correct clinical setting.

Optimized photodegradation of palm oil agroindustry waste effluent using multivalent manganese-modified black titanium dioxide.

This article presents a methodological approach to use manganese (Mn3+Mn7+)-modified black titanium dioxide (Mn/BTiO2) as a photocatalyst to optimize and improve visible-light-driven photodegradation of treated agro-industrial effluent (TPOME). A modified wet chemical process was used to prepare BTiO2. The BTiO2 was then wet impregnated with Mn and calcined at 300 °C for 1 h to produce Mn/BTiO2. The activity of Mn/BTiO2 was investigated in terms of photo-assisted elimination of chemical oxygen demand (COD), phenolic compounds (PCs), color, and total organic carbon (TOC). Using the design of experiments (DOE), the conditions of the photocatalytic process, including photocatalyst loading, Mn concentration, hydrogen peroxide (H2O2) dose, and irradiation time, were optimized. Under the optimum conditions (0.85 g/L photocatalyst loading, 0.048 mol/L H2O2 dose, 0.301 wt.% Mn concentration, and 204 min irradiation time) COD, PCs, color, and TOC removal efficiencies of 88.87%, 86.04%, 62.8%, and 84.66%, respectively, were obtained. Statistical analysis showed that the response variable's removal from TPOME estimation had high R2 and low RMSE, MSE, MAD, MAE, and MAPE values, indicating high reliability. This study demonstrated the significant potential of the developed photocatalytic system for the treatment of waste effluent generated by the palm oil industry and other agro-industries, with the ability to simultaneously reduce a number of organic pollution indicators (OPIs).

Fish collagen peptides' modulating effect on human skin microbiota against pathogenic Staphylococcus aureus.

Aim: The current research aims to design effective strategies to enhance the body's immune system against pathogenic bacteria. Methods: Skin commensals were isolated, identified and cultured in fish collagen peptides (FCPs). Results: After culturing in FCP, the skin commensals were used in a dose-dependent manner for Staphylococcus aureus in a dual-culture test, which showed significant growth inhibition of the pathogenic bacteria, which concluded that FCP induced the immune defense system of skin microbiota against pathogenic strains. Conclusion: Results have validated that fish collagen peptide plays a vital role in the growth of selected human skin flora and induces more defensive immunity against pathogenic S. aureus bacteria in dual-culture experimentation.

Fabrication of amine-functionalized and multi-layered PAN-(TiO2)-gelatin nanofibrous wound dressing: In-vitro evaluation.

The development of advanced multifunctional wound dressings remains a major challenge. Herein, a novel multilayer (ML) electrospun nanofibers (NFs) wound dressing based on diethylenetriamine (DETA) functionalized polyacrylonitrile (PAN), TiO2 nanoparticles (NPs) coating (Ct), and bioderived gelatin (Gel) was developed for potential applications in wound healing. The ML PAN-DETA-Ct-Gel membrane was developed by combining electrospinning, chemical functionalization, synthesis, and electrospray techniques, using a layer-by-layer method. The ML PAN-DETA-Ct-Gel membrane is comprised of an outer layer of PAN-DETA as a barrier to external microorganisms and structural support, an interlayer TiO2 NPs (Ct) as antibacterial function, and a contact layer (Gel) to improve biocompatibility and cell viability. The NFs membranes were characterized by scanning electron microscopy (SEM), surface profilometry, BET analysis, and water contact angle techniques to investigate their morphology, surface roughness, porosity, and wettability. The ML PAN-DETA-Ct-Gel wound dressing exhibited good surface roughness, porosity, and better wettability. Cell morphology, proliferation, and viability were determined using fibroblasts (3T3), and antibacterial assays were performed against six pathogens. The ML PAN-DETA-Ct-Gel NFs membrane showed good cell morphology, proliferation, viability, and antibacterial activity compared with other membranes. This new class of ML NFs membranes offers a multifunctional architecture with adequate biocompatibility, cell viability, and antibacterial activity.

Association of Epileptiform Activity With Outcomes in Toxic-Metabolic Encephalopathy.

The clinical significance of epileptiform abnormalities (EAs) specific to toxic-metabolic encephalopathy (TME) is unknown. OBJECTIVES: To quantify EA burden in patients with TME and its association with neurologic outcomes. DESIGN SETTING AND PARTICIPANT: This is a retrospective study. A cohort of patients with TME and EA (positive) were age, Sequential Organ Failure Assessment Score, Acute Physiology and Chronic Health Evaluation II (APACHE-II) score matched to a cohort of TME patients without EA (control). Univariate analysis compared EA-positive patients against controls. Multivariable logistical regression adjusting for underlying disease etiology was performed to examine the relationship between EA burden and probability of poor neurologic outcome (modified Rankin Score [mRS] 4-6) at discharge. Consecutive admissions to inpatient floors or ICUs that underwent continuous electroencephalography (cEEG) monitoring at a single center between 2012 and 2019. Inclusion criteria were 1) patients with TME diagnosis, 2) age greater than 18 years, and 3) greater than or equal to 16 hours of cEEG. Patients with acute brain injury and cardiac arrest were excluded. MAIN OUTCOMES AND MEASURES: Poor neurologic outcome defined by mRS (mRS 4-6). RESULTS: One hundred sixteen patients were included, 58 with EA and 58 controls without EA, where matching was performed on age and APACHE-II score. The median age was 66 (Q1-Q3, 57-75) and median APACHE II score was 18 (Q1-Q3, 13-22). Overall cohort discharge mortality was 22% and 70% had a poor neurologic outcome. Peak EA burden was defined as the 12-hour window of recording with the highest prevalence of EAs. In multivariable analysis adjusted for Charlson Comorbidity Index and primary diagnosis, presence of EAs was associated with poor outcome (odds ratio 3.89; CI [1.05-14.2], p = 0.041). Increase in peak EA burden from 0% to 100% increased probability of poor discharge neurologic outcome by 30%. CONCLUSIONS AND RELEVANCE: Increasing burden of EA is associated with worse discharge outcomes in patients with TME. Future studies are needed to determine whether short-term treatment with anti-seizure medications while medically treating the underlying metabolic derangement improves outcomes.

Detecting Blastocyst Components by Artificial Intelligence for Human Embryological Analysis to Improve Success Rate of In Vitro Fertilization.

Morphological attributes of human blastocyst components and their characteristics are highly correlated with the success rate of in vitro fertilization (IVF). Blastocyst component analysis aims to choose the most viable embryos to improve the success rate of IVF. The embryologist evaluates blastocyst viability by manual microscopic assessment of its components, such as zona pellucida (ZP), trophectoderm (TE), blastocoel (BL), and inner cell mass (ICM). With the success of deep learning in the medical diagnosis domain, semantic segmentation has the potential to detect crucial components of human blastocysts for computerized analysis. In this study, a sprint semantic segmentation network (SSS-Net) is proposed to accurately detect blastocyst components for embryological analysis. The proposed method is based on a fully convolutional semantic segmentation scheme that provides the pixel-wise classification of important blastocyst components that help to automatically check the morphologies of these elements. The proposed SSS-Net uses the sprint convolutional block (SCB), which uses asymmetric kernel convolutions in combination with depth-wise separable convolutions to reduce the overall cost of the network. SSS-Net is a shallow architecture with dense feature aggregation, which helps in better segmentation. The proposed SSS-Net consumes a smaller number of trainable parameters (4.04 million) compared to state-of-the-art methods. The SSS-Net was evaluated using a publicly available human blastocyst image dataset for component segmentation. The experimental results confirm that our proposal provides promising segmentation performance with a Jaccard Index of 82.88%, 77.40%, 88.39%, 84.94%, and 96.03% for ZP, TE, BL, ICM, and background, with residual connectivity, respectively. It is also provides a Jaccard Index of 84.51%, 78.15%, 88.68%, 84.50%, and 95.82% for ZP, TE, BL, ICM, and background, with dense connectivity, respectively. The proposed SSS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% and 86.34% with residual and dense connectivity, respectively; this shows effective segmentation of blastocyst components for embryological analysis.

Deep Features Aggregation-Based Joint Segmentation of Cytoplasm and Nuclei in White Blood Cells.

White blood cells (WBCs), also known as leukocytes, are one of the valuable parts of the blood and immune system. Typically, pathologists use microscope for the manual inspection of blood smears which is a time-consuming, error-prone, and labor-intensive procedure. To address these issues, we present two novel shallow networks: a leukocyte deep segmentation network (LDS-Net) and leukocyte deep aggregation segmentation network (LDAS-Net) for the joint segmentation of cytoplasm and nuclei in WBC images. LDS-Net is a shallow architecture with three downsampling stages and seven convolution layers. LDAS-Net is an extended version of LDS-Net that utilizes a novel pool-less low-level information transfer bridge to transfer low-level information to the deep layers of the network. This information is aggregated with deep features in a dense feature concatenation block to achieve accurate cytoplasm and nuclei joint segmentation. We evaluated our developed architectures on four WBC publicly available datasets. For cytoplasmic segmentation in WBCs, the proposed method achieved the dice coefficients of 98.97%, 99.0%, 96.05%, and 98.79% on Datasets 1, 2, 3, and 4, respectively. For nuclei segmentation, the dice coefficients of 96.35% and 98.09% are achieved for Datasets 1 and 2, respectively. Proposed method outperforms state-of-the-art methods with superior computational efficiency and requires only 6.5 million trainable parameters.

Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses.

BACKGROUND: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. METHOD: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. RESULTS: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. CONCLUSION: The proposed model is efficient and can minimize the revision complexities of implants.

Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic Analysis.

Infertility is one of the most important health concerns worldwide. It is characterized by not being successful of pregnancy after some periods of periodic unprotected sexual intercourse. In vitro fertilization (IVF) is an assisted reproduction technique that efficiently addresses infertility. IVF replaces the actual mode of reproduction through a manual procedure wherein embryos are cultivated in a controlled laboratory environment until they reach the blastocyst stage. The standard IVF procedure includes the transfer of one or two blastocysts from several blastocysts that are grown in a controlled environment. The morphometric properties of blastocysts with their compartments such as trophectoderm (TE), zona pellucida (ZP), inner cell mass (ICM), and blastocoel (BL), are analyzed through manual microscopic analysis to predict viability. Deep learning has been extensively used for medical diagnosis and analysis and can be a powerful tool to automate the morphological analysis of human blastocysts. However, the existing approaches are inaccurate and require extensive preprocessing and expensive architectures. Thus, to cope with the automatic detection of blastocyst components, this study proposed a novel multiscale aggregation semantic segmentation network (MASS-Net) that combined four different scales via depth-wise concatenation. The extensive use of depthwise separable convolutions resulted in a decrease in the number of trainable parameters. Further, the innovative multiscale design provided rich spatial information of different resolutions, thereby achieving good segmentation performance without a very deep architecture. MASS-Net utilized 2.06 million trainable parameters and accurately detects TE, ZP, ICM, and BL without using preprocessing stages. Moreover, it can provide a separate binary mask for each blastocyst component simultaneously, and these masks provide the structure of each component for embryonic analysis. Further, the proposed MASS-Net was evaluated using publicly available human blastocyst (microscopic) imaging data. The experimental results revealed that it can effectively detect TE, ZP, ICM, and BL with mean Jaccard indices of 79.08, 84.69, 85.88%, and 89.28%, respectively, for embryological analysis, which was higher than those of the state-of-the-art methods.