Diagnostic Utility of Swept-Source OCT-Based Biometry and Fundus Photographs Compared to Spectral Domain OCT in Center-Involving Diabetic Macular Edema.

PURPOSE: This study was aimed to evaluate the agreement between the swept-source optical coherence tomography (SS-OCT)-based biometry, fundus photographs, and their combination, in comparison to the gold standard spectral-domain optical coherence tomography (SD-OCT) for the detection of center-involving diabetic macular edema (CI-DME). METHODS: We conducted a retrospective cross-sectional study involving 55 subjects (78 eyes) diagnosed with diabetic macular edema (DME) detected clinically and on SD-OCT (Carl Zeiss Meditec AG). Post-mydriatic 45-degree color fundus photograph (Crystal-Vue NFC-700), 1 mm macular scan obtained from SS-OCT-based biometry (IOL-Master 700), and macula cube scan obtained from SD-OCT was used to detect and grade DME into CI-DME and NCI-DME. RESULTS: Our findings revealed that SS-OCT-based biometry was noted to have a high sensitivity of 1 (0.94-1.00) and a specificity of 0.63 (0.31-0.89) in detecting CI-DME compared to the gold standard (SD-OCT). When combined with data from fundus photographs, specificity decreased to 0.32 (0.15-0.53). Fundus photographs alone exhibited a low sensitivity of 0.52 (0.38-0.64) and a specificity of 0.45 (0.16-0.76) in CI-DME detection. CONCLUSION: In conclusion, SS-OCT-based biometry can be used as an effective tool for the detection of CI-DME in diabetic patients undergoing cataract surgery and can serve as a screening tool in centers without SD-OCT facilities.

Diabetic Macular Edema (DME); Center Involving Diabetic Macular Edema (CI-DME); Non-Center Involving Diabetic Macular Edema (NCI-DME); Swept-Source Optical Coherence Tomography (SS-OCT); Spectral-Domain Optical Coherence Tomography (SD-OCT); Anti-Vascular Endothelial Growth Factor (Anti-VEGF); Central Retinal Thickness (CRT); Intra Retinal Fluid (IRF); Sub Retinal Fluid (SRF); Diabetic Retinopathy (DR); Non Proliferative Diabetic Retinopathy (NPDR); Proliferative Diabetic Retinopathy (PDR); Best Corrected Visual Acuity (BCVA); Glycosylated hemoglobin (HbA1c); Mean Spherical Error (MSE); Standard Deviation (SD); Positive Predictive value (PPV); Predictive value (PPV); Negative predictive value (NPV); Area under the Curve (AUC).

Association of eGFR with stages of diabetic retinopathy and age-related macular degeneration in Indian population.

PURPOSE: To investigate the influence of glomerular filtration rate in renal disease decline and its association with diabetic retinopathy (DR) and age-related macular degeneration (ARMD) in patients in South India. METHODS: A population-based cross-sectional study was conducted including participants with DR and ARMD recruited from urban and rural populations. The data collection included medical history, anthropometric measurements, and ophthalmic work-up. The estimated glomerular filtration rate (eGFR) was calculated using the equation of chronic kidney disease-epidemiology collaboration (CKD-EPI). The grading of AMD was done by a single experienced (more than 5 years) vitreoretinal surgeon as per the International ARM Epidemiological Study Group and it was staged based on grading in the worsened eye. RESULTS: A decline in eGFR was observed as the severity of DR increased ( P < 0.001). Baseline characteristics such as age ( P < 0.001), duration of diabetes ( P < 0.001), gender ( P < 0.001), creatinine ( P < 0.001), albumin to creatinine ratio (ACR; P < 0.001), albuminuria ( P = 0.023), blood urea ( P < 0.001), and high-density lipoprotein (HDL; P = 0.003) were found to be statistically significant. The risk for developing DR with CKD was found to be 5 times higher in male patients compared to female patients. Age and high blood urea level, diastolic blood pressure, mild and moderate DR were the risk factors associated with CKD. A decline in eGFR was observed as the severity of ARMD increased ( P < 0.001). The risk factors associated with CKD were age, gender, smoking, alcohol consumed, presence of hypertension, duration of diabetes, systolic and diastolic blood pressure, history of diabetes, body mass index (BMI), serum triglycerides, and serum HDL cholesterol. CONCLUSION: Reduced eGFR values were associated with an increase in the severity of DR and ARMD.

Deep learning-based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis.

Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94-0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90-0.96).

Diffusiophoresis-enhanced particle deposition for additive manufacturing.

The ability to govern particle assembly in an evaporative-driven additive manufacturing (AM) can realize multi-scale features fundamental to creating printed electronics. However, existing techniques remain challenging and often require templates or contaminating solutes. We explore the control of particle deposition in 3D-printed colloids by diffusiophoresis, a previously unexplored mechanism in multi-scale AM. Diffusiophoresis can introduce spontaneous phoretic particle motion by establishing local solute concentration gradients. We show that diffusiophoresis can play a dominant role in complex evaporative-driven particle assembly, enabling a fundamentally new and versatile control of particle deposition in a multi-scale AM process.

Aesthetic Perception of Patients and Dentists on Conventional and Characterized Complete Dentures.

Objective: The objective of the study was to compare the perception of patients and dentists regarding the esthetics achieved with conventional and characterized complete dentures. Materials and Methods: Twenty completely edentulous participants were selected and given both conventional and characterized complete dentures. Participants including patients wearing the dentures and dentists subsequently evaluated both types of dentures by giving scores using 0-10 cm Visual Analog Scale on denture esthetics. The esthetic scores were statistically analyzed using paired t-test (P = 0.05 as significant). Results: Patients' satisfaction with conventional and characterized complete dentures were statistically similar except for the fact that patients were more satisfied with ideal arrangement of teeth in conventional denture (standard deviation [SD]-7.0 ± 2,0) compared to the characterized (SD-4.8 ± 2.7) arrangement of the teeth (P = 0.038). Conclusions: Aesthetic satisfaction with complete dentures was influenced by the patients' socioeconomic status. Patients could not see any significant difference between conventional and characterized complete denture.

"Combination of Thoracic Epidural Anesthesia, Supraclavicular Brachial Plexus Block and Supraclavicular Nerve Block as Surgical Anesthesia for Modified Radical Mastectomy-A Case Series".

Sensory innervation of the breast is complex, thereby making it difficult to perform any surgical procedure with a single regional anesthesia technique. Because of the involvement of pectoral muscles and extension of the incision into the axilla, a modified radical mastectomy makes it further challenging and requires multiple techniques. We have used a new combination of regional techniques in this case series and found that it provided complete surgical anesthesia with a smaller volume of local anesthetic.

Machine learning-enabled feature classification of evaporation-driven multi-scale 3D printing.

The freeform generation of active electronics can impart advanced optical, computational, or sensing capabilities to an otherwise passive construct by overcoming the geometrical and mechanical dichotomies between conventional electronics manufacturing technologies and a broad range of three-dimensional (3D) systems. Previous work has demonstrated the capability to entirely 3D print active electronics such as photodetectors and light-emitting diodes by leveraging an evaporation-driven multi-scale 3D printing approach. However, the evaporative patterning process is highly sensitive to print parameters such as concentration and ink composition. The assembly process is governed by the multiphase interactions between solutes, solvents, and the microenvironment. The process is susceptible to environmental perturbations and instability, which can cause unexpected deviation from targeted print patterns. The ability to print consistently is particularly important for the printing of active electronics, which require the integration of multiple functional layers. Here we demonstrate a synergistic integration of a microfluidics-driven multi-scale 3D printer with a machine learning algorithm that can precisely tune colloidal ink composition and classify complex internal features. Specifically, the microfluidic-driven 3D printer can rapidly modulate ink composition, such as concentration and solvent-to-cosolvent ratio, to explore multi-dimensional parameter space. The integration of the printer with an image-processing algorithm and a support vector machine-guided classification model enables automated, in-situ pattern classification. We envision that such integration will provide valuable insights in understanding the complex evaporative-driven assembly process and ultimately enable an autonomous optimisation of printing parameters that can robustly adapt to unexpected perturbations.

Prevalence of Oral Habits and its Association with Malocclusion in Primary Dentition among School Going Children of Nepal.

OBJECTIVES: The objectives of this study were to: (1) study the prevalence of oral habits among school going children with primary dentition; (2) determine the association of oral habits with malocclusion in primary dentition; and (3) compare the prevalence of oral habits based on gender, race, age and grade. STUDY DESIGN: A community based cross-sectional study was conducted among 625 school going children with a complete set of primary dentitions. A closed-ended questionnaire was developed to gather information about oral habits followed by clinical examination. RESULTS: The prevalence of oral habits was 42.7%. Finger nail biting (19.5%) was the most prevalent oral habit, whereas self-destructive oral habits (0.7%) were the least prevalent. Multivariate logistic regression analysis revealed significant association of (P<0.05): digit sucking habit with distal step molar relationship, class II canine relationship and increased overjet; pacifier sucking habit with presence of distal step molar relationship, class II canine relationship, crossbite and increased overjet; and finger nail biting habit with absence of primate spaces. CONCLUSIONS: Nearly half of the participating children indulged in one or more oral habits. There was significant association between some oral habits and malocclusion traits, indicating the requirement of timely screening and interception.

Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials.

Conventional approaches to developing biomaterials and implants require intuitive tailoring of manufacturing protocols and biocompatibility assessment. This leads to longer development cycles, and high costs. To meet existing and unmet clinical needs, it is critical to accelerate the production of implantable biomaterials, implants and biomedical devices. Building on the Materials Genome Initiative, we define the concept 'biomaterialomics' as the integration of multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools throughout the entire pipeline of biomaterials development. The Data Science-driven approach is envisioned to bring together on a single platform, the computational tools, databases, experimental methods, machine learning, and advanced manufacturing (e.g., 3D printing) to develop the fourth-generation biomaterials and implants, whose clinical performance will be predicted using 'digital twins'. While analysing the key elements of the concept of 'biomaterialomics', significant emphasis has been put forward to effectively utilize high-throughput biocompatibility data together with multiscale physics-based models, E-platform/online databases of clinical studies, data science approaches, including metadata management, AI/ Machine Learning (ML) algorithms and uncertainty predictions. Such integrated formulation will allow one to adopt cross-disciplinary approaches to establish processing-structure-property (PSP) linkages. A few published studies from the lead author's research group serve as representative examples to illustrate the formulation and relevance of the 'Biomaterialomics' approaches for three emerging research themes, i.e. patient-specific implants, additive manufacturing, and bioelectronic medicine. The increased adaptability of AI/ML tools in biomaterials science along with the training of the next generation researchers in data science are strongly recommended. STATEMENT OF SIGNIFICANCE: This leading opinion review paper emphasizes the need to integrate the concepts and algorithms of the data science with biomaterials science. Also, this paper emphasizes the need to establish a mathematically rigorous cross-disciplinary framework that will allow a systematic quantitative exploration and curation of critical biomaterials knowledge needed to drive objectively the innovation efforts within a suitable uncertainty quantification framework, as embodied in 'biomaterialomics' concept, which integrates multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools, like machine learning. The formulation of this approach has been demonstrated for patient-specific implants, additive manufacturing, and bioelectronic medicine.

Critical comparison of image analysis workflows for quantitative cell morphological evaluation in assessing cell response to biomaterials.

Quantitative image analysis is an important tool in understanding cell fate processes through the study of cell morphological changes in terms of size, shape, number, and orientation. In this context, this work explores systematically the main challenges involved in the quantitative analysis of fluorescence microscopy images and also proposes a new protocol while comparing its outcome with the widely used ImageJ analysis. It is important to mention that fluorescence microscopy is by far most widely used in biocompatibility analysis (observing cell fate changes) of implantable biomaterials. In this study, we employed two different image analyses toolsets: (a) the conventionally employed ImageJ software, and (b) a recently developed automated digital image analyses framework, called ImageMKS. While ImageJ offers a powerful toolset for image analyses, it requires sophisticated user expertise to design and iteratively refine the analyses workflow. This workflow primarily comprises a sequence of image transformations that typically involve de-noising and labeling of features. On the other hand, ImageMKS automates the image analyses protocol to a large extent, and thereby mitigates the influence of the user bias on the final results. This aspect is addressed using a case study of C2C12 mouse myoblast cells grown on poly(vinylidene difluoride) (PVDF) based polymeric substrates. In particular, we used a number of fluorescence microscopy images of these mouse myoblasts grown on PVDF-based nanobiocomposites under the influence of electric field. In addition to the MKS workflows requiring much less user time because of their automation, it was observed that ImageMKS workflows consistently produced more reliable results that correlated better with the previously reported experimental studies.

Experimental investigation of the adsorption and desorption of cellulase enzymes on zeolite-ß for enzyme recycling applications.

The recyclability of cellulase enzymes using zeolite and polyethylene glycol (PEG) was investigated. The cellulase enzymes from cellulose hydrolysate suspensions were adsorbed onto zeolite-ß under typical working conditions (pH 5). PEG having a molecular weight of 200 Da and 20 kDa was used as an eluent to desorb the cellulase enzymes from zeolite-ß. Adsorption and desorption profiles of cellulase enzymes were studied by varying pH, PEG concentration, and salt concentration. Maximum binding capacity, qm of the zeolite decreased by increasing the pH, or by introducing PEG. At pH 5, the qm of the zeolite was determined to be 121 × 10-3 g/g. About 24%, 51% and 75% of the adsorbed enzyme can be recovered using 1 M NaCl, PEG 200 and PEG 20000, respectively. The specific activity of the recovered enzyme increased by 57% due to the presence of residual PEG.

Evaluation of Ti-Mn Alloys for Additive Manufacturing Using High-Throughput Experimental Assays and Gaussian Process Regression.

Compositionally graded cylinders of Ti-Mn alloys were produced using the Laser Engineered Net Shaping (LENS™) technique, with Mn content varying from 0 to 12 wt.% along the cylinder axis. The cylinders were subjected to different post-build heat treatments to produce a large sample library of a-b microstructures. The microstructures in the sample library were studied using back-scattered electron (BSE) imaging in a scanning electron microscope (SEM), and their mechanical properties were evaluated using spherical indentation stress-strain protocols. These protocols revealed that the microstructures exhibited features with averaged chord lengths in the range of 0.17-1.78 mm, and beta content in the range of 20-83 vol.%. The estimated values of the Young's moduli and tensile yield strengths from spherical indentation were found to vary in the ranges of 97-130 GPa and 828-1864 MPa, respectively. The combined use of the LENS technique along with the spherical indentation protocols was found to facilitate the rapid exploration of material and process spaces. Analyses of the correlations between the process conditions, several key microstructural features, and the measured material properties were performed via Gaussian process regression (GPR). These data-driven statistical models provided valuable insights into the underlying correlations between these variables.

Voxelized Atomic Structure Potentials: Predicting Atomic Forces with the Accuracy of Quantum Mechanics Using Convolutional Neural Networks.

This paper introduces voxelized atomic structure (VASt) potentials as a machine learning (ML) framework for developing interatomic potentials. The VASt framework utilizes a voxelized representation of the atomic structure directly as the input to a convolutional neural network (CNN). This allows for high-fidelity representations of highly complex and diverse spatial arrangements of the atomic environments of interest. The CNN implicitly establishes the low-dimensional features needed to correlate each atomic neighborhood to its net atomic force. The selection of the salient features of the atomic structure (i.e., feature engineering) in the VASt framework is implicit, comprehensive, automated, scalable, and highly efficient. The calibrated convolutional layers learn the complex spatial relationships and multibody interactions that govern the physics of atomic systems with remarkable fidelity. We show that VASt potentials predict highly accurate forces on two phases of silicon carbide and the thermal conductivity of silicon over a range of isotropic strain.

Chemical Modification of Alginate for Controlled Oral Drug Delivery.

Here, we report two methods that chemically modify alginate to achieve neutral-basic pH sensitivity of the resultant hydrogel. The first method involves direct amide bond formation between alginate and 4-(2-aminoethyl)benzoic acid. The second method that arose out of the desire to achieve better control of the degradation rate of the alginate hydrogel involves reductive amination of oxidized alginate. The products of both methods result in a hydrogel vehicle for targeted delivery of encapsulated payload under physiological conditions in the gastrointestinal tract. Two-dimensional diffusion-ordered spectroscopy and internal and coaxial external nuclear magnetic resonance standards were used to establish chemical bonding and percent incorporation of the modifying groups into the alginate polymer. The hydrogel made with alginate modified by each method was found to be completely stable under acidic pH conditions while disintegrating within minutes to hours in neutral-basic pH conditions. We found that, while alginate oxidation did not affect the ß-d-mannuronate/α-l-guluronate ratio of alginate, the rate of disintegration of the hydrogel made with oxidized alginate was dependent upon the degree of oxidation.

Prevalence of Oral Habits of Schoolchildren in the Mixed Dentition in Dharan, Nepal.

Purpose: To determine the prevalence of oral habits in the mixed dentition of schoolchildren in Dharan, Nepal and compare this prevalence between sexes, races, and age.
Methods: A cross-sectional study was conducted among 830 schoolchildren of 10 randomly selected schools in Dharan. The presence or absence of oral habits was determined using a structured questionnaire, and an oral examination of the children was carried out under natural light. A chi-square test was used to compare the association of oral habits with age, sex and race.
Results: The most prevalent oral habit was tongue thrusting (39.6 percent), while the habit of biting objects was the least prevalent (0.8 percent). Mouth-breathing was statistically significant for sex (P = 0.010), whereas biting objects (P = 0.035) and self-inflicting habits (P = 0.005) were statistically significant for race. Lip sucking (P ≤0.001) and lip-biting habits (P = 0.001) were statistically significant for age.
Conclusion: These findings highlight the importance of periodic screenings and timely referral for the early detection of oral habits and its associated effect on the developing occlusion.

Periprosthetic biomechanical response towards dental implants, with functional gradation, for single/multiple dental loss.

The differences in shape and stiffness of the dental implants with respect to the natural teeth (especially, dental roots) cause a significant alteration of the periprosthetic biomechanical response, which typically leads to bone resorption and ultimately implant loosening. In order to avoid such clinical complications, the implant stiffness needs to be appropriately adapted. In this study, hollow channels were virtually introduced within the designed implant screws for reduction of the overall stiffness of the prototype. In particular, two opposing radial gradients of increasing hollow channel diameters, i.e., outside to inside (Channel 1) and inside to outside (Channel 2) were considered. Two clinical situations of edentulism were addressed in this finite element-based study, and these include a) loss of the first molar, and b) loss of all the three molars. Consequently, two implantation approaches were simulated for multiple teeth loss - individual implantation and implant supported dental bridge. The effects of implant length, approach and channel distribution on the biomechanical response were evaluated in terms of the von Mises stress within the interfacial periprosthetic bone, under normal masticatory loading. The results of our FE analysis clearly reveal significant variation in periprosthetic bone stress between the different implant designs and approaches. An implant screw length of 11 mm with the Channel 2 configuration was found to provide the best biomechanical response. This study also revealed that the implant supported dental bridge approach, which requires lower bone invasion, results in favorable biomechanical response in case of consecutive multiple dental loss.

Histopathological Review of Diagnostic Categories of the Bethesda System for Reporting Thyroid Cytopathology - An Institutional Experience of 5 Years.

CONTEXT: Fine needle aspiration (FNA) plays a crucial role in the evaluation of patients with thyroid lesions. The Bethesda system for reporting thyroid cytopathology (TBSRTC) was designed with a mission to standardize the process of diagnosis and management of thyroid lesions by FNA cytology (FNAC). AIM: We aim to see the benefits of adopting TBSRTC, seek the cytological pitfalls in the diagnosis of thyroid FNAC, and identify the spectrum of thyroid lesions in our setup. SETTINGS AND DESIGN: This is a hospital-based cross-sectional study conducted from June 2009 to June 2014 of all thyroid FNACs with available histopathology reports. Cases were designated a specific diagnostic category according to TBSRTC. MATERIALS AND METHODS: A total of 109 cases were included in the study. Sixty-eight cases had been reported without using TBSRTC and were reviewed and reclassified according to TBSRTC seeking the common reasons for interpretative errors. STATISTICAL ANALYSIS USED: Data were analyzed using SPSS ver. 11.5. RESULTS: In both pre- and post-TBSRTC era, benign neoplasms constituted the major bulk. After the use of TBSRTC, there was increased ability to look for follicular neoplasms, improvement in making definitive diagnosis of the cases, decline in the suspicious category, and an improvement in diagnostic accuracy, and we were in line with the implied risk outlined by TBSRTC in most of the cases except the nondiagnostic or unsatisfactory category. CONCLUSION: Application of TBSRTC results in uniformity in reporting among pathologists and better interdisciplinary communication and patient management.

Trends in incidence of Ewing sarcoma of bone in India - Evidence from the National Cancer Registry Programme (1982-2011).

BACKGROUND: Ewing sarcoma is a malignant tumour found mainly in childhood and adolescence. The present study aims at analyzing the data on Ewing sarcoma cases of bone from the National Cancer Registry Programme, India to provide incidence, patterns, and trends in the Indian population. MATERIALS AND METHODS: The data of five Population Based Cancer Registries (PBCR) of Bangalore, Mumbai, Chennai, Bhopal and Delhi over 30 years period (1982- 2011) were used to calculate the Age Specific and Age Standardized Incidence Rates (ASpR and ASIR), and trends in incidence was analyzed by linear and Joinpoint Regression. RESULTS: Ewing sarcoma comprised around 15 % of all bone malignancies. Sixty-eight percent were 0-19 years, with 1.6 times risk of tumour in bones of limbs as compared to other bones. The highest incidence rate (per million) was in the 10-14 years age group (male -4.4, female -2.9) with significantly increasing trend in ASpR observed in both sexes. Pooled ASIR per million for all ages was higher in male (1.6) than female (1.0) with an increasing rate ratio of ASIR with increase in age. Trend of pooled ASIR for all ages was significantly increased in both sexes. Twelve percent cases were reported in ≥30 years of age. CONCLUSION: This paper has described population based measurements on burden and trends in incidence of skeletal Ewing in India. These may steer further research questions on the clinical and molecular epidemiology to explain factors associated with the increasing incidence of Ewing sarcoma bone observed in India.

Probing nanoscale damage gradients in ion-irradiated metals using spherical nanoindentation.

We discuss and demonstrate the application of recently developed spherical nanoindentation stress-strain protocols in characterizing the mechanical behavior of tungsten polycrystalline samples with ion-irradiated surfaces. It is demonstrated that a simple variation of the indenter size (radius) can provide valuable insights into heterogeneous characteristics of the radiation-induced-damage zone. We have also studied the effect of irradiation for the different grain orientations in the same sample.