Diagnostic and Prognostic Utility of Non-Invasive Multimodal Imaging in Chronic Wound Monitoring: a Systematic Review.

Monitoring chronic wound [CW] healing is a challenging issue for clinicians across the world. Moreover, the health and cost burden of CW are escalating at a disturbing rate due to a global rise in population of elderly and diabetic cases. The conventional approach includes visual contour, sketches, or more rarely tracings. However, such conventional techniques bring forth infection, pain, allergies. Furthermore, these methods are subjective as well as time-consuming. As such, nowadays, non-touching and non-invasive CW monitoring system based on imaging techniques are gaining importance. They not only reduce patients' discomfort but also provide rapid wound diagnosis and prognosis. This review provides a survey of different types of CW characteristics, their healing mechanism and the multimodal non-invasive imaging methods that have been used for their diagnosis and prognosis. Current clinical practices as well as personal health systems [m-health and e-health] for CW monitoring have been discussed.

HOXA-11 mediated dysregulation of matrix remodeling during implantation window in women with endometriosis.

PURPOSE: To investigate the Homeobox genes HOXA-10 and HOXA-11 mediated endometrial molecular defects during implantation window in endometriosis-associated infertility cases. METHODS: Endometrial biopsies were obtained during implantation window from 31 infertile women with endometriosis (age < 35 years) and 26 age and BMI-matched infertile women without endometriosis were included in the study for comparison purposes. Endometrial expression of HOXA-10 and HOXA-11 genes, MMP-2, -9, α(v)ß(3) integrin, leukemia inhibitory factor and surface characteristics including average roughness and topology were assessed. RESULTS: A significantly lower expression of HOXA-10 and HOXA-11 were observed in endometriotic women compared to non-endometriotic controls. Further, a significantly higher endometrial expression of MMP-2 and -9 were observed in women with endometriosis when compared with controls. Interestingly, endometrial surface were observed to be grossly affected in terms of average roughness and topology in women with endometriosis compared to controls. CONCLUSIONS: The findings suggest that aberrant expression of HOXA-10 and -11 genes adversely affects endometrial remodelling and expression of receptivity markers.

Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals.

Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.

"Omics" in oral cancer: New approaches for biomarker discovery.

OBJECTIVES: In this review paper, we explored the application of "omics" approaches in the study of oral cancer (OC). It will provide a better understanding of how "omics" approaches may lead to novel biomarker molecules or molecular signatures with potential value in clinical practice. A future direction of "omics"-driven research in OC is also discussed. METHODS: Studies on "omics"-based approaches [genomics/proteomics/transcriptomics/metabolomics] were investigated for differentiating oral squamous cell carcinoma,oral sub-mucous fibrosis, oral leukoplakia, oral lichen planus, oral erythroplakia from normal cases. Electronic databases viz., PubMed, Springer, and Google Scholar were searched. RESULTS: One eighty-one studies were included in this review. The review shows that the fields of genomics, transcriptomics, proteomics, and metabolomics-based marker identification have implemented advanced tools to screen early changes in DNA, RNA, protein, and metabolite expression in OC population. CONCLUSIONS: It may be concluded that despite advances in OC therapy, symptomatic presentation occurs at an advanced stage, where various curative treatment options become very limited. A molecular level study is essential for detecting an OC biomarker at an early stage. Modern "Omics" strategies can potentially make a major contribution to meet this need.

Serum-based diagnostic prediction of oral submucous fibrosis using FTIR spectrometry.

Oral submucous fibrosis (OSF) is found to have the highest malignant potentiality among all other pre-cancerous lesions. However, its detection prior to tissue biopsy can be challenging in clinics. Moreover, biopsy examination is invasive and painful. Hence, there is an urgent need of new technology that facilitates accurate diagnostic prediction of OSF prior to biopsy. Here, we used FTIR spectroscopy coupled with chemometric techniques to distinguish the serum metabolic signatures of OSF patients (n=30) and healthy controls (n=30). Serum biochemical analyses have been performed to further support the FTIR findings. Absorbance intensities of 45 infrared wavenumbers differed significantly between OSF and normal serum FTIR spectra representing alterations in carbohydrates, proteins, lipids and nucleic acids. Nineteen prominent significant wavenumbers (P≤0.001) at 1020, 1025, 1035, 1039, 1045, 1078, 1055, 1100, 1117, 1122, 1151, 1169, 1243, 1313, 1398, 1453, 1544, 1650 and 1725cm-1 provided excellent segregation of OSF spectra from normal using multivariate statistical techniques. These findings provided essential information on the metabolic features of blood serum of OSF patients and established that FTIR spectroscopy coupled with chemometric analysis can be potentially useful in the rapid and accurate preoperative screening/diagnosis of OSF.

Computer-aided diagnosis of breast cancer using cytological images: A systematic review.

Cytological evaluation by microscopic image-based characterization [imprint cytology (IC) and fine needle aspiration cytology (FNAC)] plays an integral role in primary screening/detection of breast cancer. The sensitivity of IC and FNAC as a screening tool is dependent on the image quality and the pathologist's level of expertise. Computer-aided diagnosis (CAD) is used to assists the pathologists by developing various machine learning and image processing algorithms. This study reviews the various manual and computer-aided techniques used so far in breast cytology. Diagnostic applications were studied to estimate the role of CAD in breast cancer diagnosis. This paper presents an overview of image processing and pattern recognition techniques that have been used to address several issues in breast cytology-based CAD including slide preparation, staining, microscopic imaging, pre-processing, segmentation, feature extraction and diagnostic classification. This review provides better insights to readers regarding the state of the art the knowledge on CAD-based breast cancer diagnosis to date.

Nanoscale Surface Characterization of Human Erythrocytes by Atomic Force Microscopy: A Critical Review.

Erythrocytes (red blood cells, RBCs), the most common type of blood cells in humans are well known for their ability in transporting oxygen to the whole body through hemoglobin. Alterations in their membrane skeletal proteins modify shape and mechanical properties resulting in several diseases. Atomic force microscopy (AFM), a new emerging technique allows non-invasive imaging of cell, its membrane and characterization of surface roughness at micrometer/nanometer resolution with minimal sample preparation. AFM imaging provides direct measurement of single cell morphology, its alteration and quantitative data on surface properties. Hence, AFM studies of human RBCs have picked up pace in the last decade. The aim of this paper is to review the various applications of AFM for characterization of human RBCs topology. AFM has been used for studying surface characteristics like nanostructure of membranes, cytoskeleton, microstructure, fluidity, vascular endothelium, etc., of human RBCs. Various modes of AFM imaging has been used to measure surface properties like stiffness, roughness, and elasticity. Topological alterations of erythrocytes in response to different pathological conditions have also been investigated by AFM. Thus, AFM-based studies and application of image processing techniques can effectively provide detailed insights about the morphology and membrane properties of human erythrocytes at nanoscale.

Morphometric evaluation of preeclamptic placenta using light microscopic images.

Deficient trophoblast invasion and anomalies in placental development generally lead to preeclampsia (PE) but the inter-relationship between placental function and morphology in PE still remains unknown. The aim of this study was to evaluate the morphometric features of placental villi and capillaries in preeclamptic and normal placentae. The study included light microscopic images of placental tissue sections of 40 preeclamptic and 35 normotensive pregnant women. Preprocessing and segmentation of these images were performed to characterize the villi and capillaries. Fisher's linear discriminant analysis (FLDA), hierarchical cluster analysis (HCA), and principal component analysis (PCA) were applied to identify the most significant placental (morphometric) features from microscopic images. A total of 10 morphometric features were extracted, of which the villous parameters were significantly altered in PE. FLDA identified 5 highly significant morphometric features (>90% overall discrimination accuracy). Two large subclusters were clearly visible in HCA based dendrogram. PCA returned three most significant principal components cumulatively explaining 98.4% of the total variance based on these 5 significant features. Hence, quantitative microscopic evaluation revealed that placental morphometry plays an important role in characterizing PE, where the villous is the major component that is affected.

Topological features of erythrocytes in thalassemic patients: quantitative characterization by scanning electron and atomic force microscopy.

OBJECTIVE: To examine the efficiency of scanning electron microscopy (SEM) and atomic force microscopy (AFM) in investigating structural anomalies of thalassemic erythrocytes. STUDY DESIGN: Erythrocytes or red blood cells (RBCs) separated from blood samples of 35 healthy and 42 thalassemic individuals were processed for SEM and AFM imaging, respectively. SEM images were taken after erythrocytes fixed on cover slips were coated with gold. Alterations in both 2D and 3D surface features of erythrocytes were examined with AFM in tapping mode. Fractal dimension was used to estimate erythrocytes surface roughness from SEM and AFM images. RESULTS: SEM and AFM images showed that healthy erythrocytes were uniform, exhibiting a typical circular and biconcave shape. Thalassemic erythrocytes were notably smaller and the central part was swollen, resulting in irregularity. In the case of SEM images it was observed that there was significant increase (p < 0.001) in roughness of thalassemic erythrocytes. Surface roughness parameters of thalassemic erythrocytes in AFM images were significantly higher (p < 0.001) as compared to healthy ones. CONCLUSION: Surface characteristics of erythrocytes are important determinants for distinguishing thalassemic RBCs. Both SEM and AFM images revealed morphological deformities of thalassemic erythrocytes. AFM proved to be a powerful technique for topographical characterization of abnormal erythrocytes.

Altered metabolic profile in early and late onset preeclampsia: An FTIR spectroscopic study.

OBJECTIVE: Metabolic anomalies, if any, between early and late onset preeclampsia [PE] were explored using Fourier transform infrared [FTIR] spectroscopy. SETTING: Department of Gynecology and Obstetrics, SSKM Hospital, IPGMER, Kolkata and Midnapur Medical College Hospital, Midnapur, India. SAMPLE: 80 pregnant women attending routine antenatal care units; (i) early onset PE [gestational age; GA<34weeks] (ii) late onset PE [GA>34weeks] (iii) early onset control [GA 24-34weeks] and (iv) late onset control [GA>34weeks]. METHODS: Serum FTIR spectra were obtained in the wave-number range of 600-4000cm(-1) at 4cm(-1) resolution. (1)H NMR and estimation of atherosclerotic index (AI) were performed to validate the FTIR findings. MAIN OUTCOME MEASURE(S): Clinical characteristics and metabolic profile. RESULTS: 13 spectral peaks corresponding to the carbohydrate, protein and lipid region were significantly altered in early onset PE [P<0.001; at 95% confidence interval]. Discriminant analysis identified five highly significant wave-numbers (1078, 1088, 1122, 1169 and 1171cm(-1)) having ⩾80% overall accuracy. Hierarchical cluster analysis of the obtained spectra at these 5 wave-numbers provided excellent segregation of early and late onset PE with respect to their controls. Principal component analysis revealed that these 5 wave-numbers significantly separated the two sub-groups of PE (97.95% of the total variance). (1)H NMR results showed that serum levels of glutamate, choline, alanine and lactate were significantly higher while ariginine and citrate were significantly decreased in early onset PE as compared to late onset cases. CONCLUSION: Our study reveals differences in metabolomic profiles of early and late onset preeclamptic cases.

Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding.

The paper proposes a robust approach to automatic segmentation of leukocyte's nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert hematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises, respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm.

Automated tissue classification framework for reproducible chronic wound assessment.

The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the "S" component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793).

Synonymous codon usage pattern analysis of Hepatitis D virus.

Hepatitis D virus (HDV) is the smallest animal infecting RNA virus with unique features distinguishing it from other Hepatitis viruses. Codon usage variation is considered as an indicator of the forces shaping genome evolution. RSCU (relative synonymous codon usage) values, nucleotide contents, ENC (effective number of codons) values, aromaticity and hydrophobicity of 28 HDV sequences were calculated and compared. RSCU values revealed that most of the codons ended with G or C. A comparative analysis of codon usage between HDV and human cells indicated that the synonymous codon usage pattern of HDV is a mixture of coincidence and antagonism to that of host cell. Finally the characteristics of the synonymous codon usage patterns, ENC plot and the correlation analysis revealed that the most important determinant of the codon usage pattern for HDV is mutational pressure and positive selection force might have some influence in sequence diversity. Comparison of ENC values and GC frequencies at 3rd codon position (GC3s) between HDV and other Hepatitis viruses indicated that HDV comprise a distinct entity.

Posterior capsular opacification and intraocular lens surface micro-roughness characteristics: an atomic force microscopy study.

OBJECTIVE: Surface roughness parameters of various intraocular lenses (IOLs) biomaterials using atomic force microscopy (AFM) are compared. Variation, if any, in the micro-roughness properties of different IOLs made up of the same biomaterial is also explored. Retrospective analysis of posterior capsular opacification (PCO) incidence has been followed up for a period of four years post IOL implantation to evaluate the correlation of PCO formation with surface roughness of IOLs. DESIGN: Experimental materials study. MATERIALS AND PARTICIPANTS: Surface characteristics of 20 different IOL models were assessed using AFM. These IOL models were made up of PMMA or HEMA or acrylic hydrophobic or acrylic hydrophilic or silicone. Retrospective analysis of PCO incidence in 3629 eyes of 2656 patients implanted with the same IOL models was performed. METHODS: Topological characteristics of 20 different IOLs made up of 5 different biomaterials including (i) PMMA, (ii) HEMA, (iii) acrylic hydrophobic, (iv) acrylic hydrophilic and (v) silicone were evaluated using AFM in the tapping mode. Images were acquired with a resolution of 256 × 256 data points per scan at a scan rate of 0.5 Hz per line and a scan size of 10 × 10µm. Rate of PCO formation in 3629 eyes of 2656 patients implanted with the five different IOL biomaterials was retrospectively analyzed. RESULTS: AFM images of IOL optic surfaces showed a collection of pores, grooves, ridges and surface irregularities. Surface roughness parameters of the IOL optics were significantly different on comparing lenses of different materials. Acrylic hydrophobic IOLs had minimum surface roughness while acrylic hydrophilic IOLs showed the highest surface roughness. Different IOL models of the same biomaterial showed varied topological roughness characteristics. Retrospective analyses of PCO formation rate after IOL implantation was carried out, which revealed that rate of PCO incidence, was directly proportional to the increase in surface micro-roughness of IOLs. CONCLUSIONS: AFM is a powerful technique for the topological characterization of IOLs. Acrylic hydrophobic IOLs showed minimum surface roughness properties as well as minimum PCO incidence over a period of four years post implantation. It is, therefore, tempting to consider acrylic hydrophobic IOLs over other IOL biomaterials as the ideal biocompatible material for lowering PCO incidence. These results suggest an urgent need for manufacturers to optimize the various steps involved in the fabrication of IOLs.

Comparison of oral dydrogestrone with progesterone gel and micronized progesterone for luteal support in 1,373 women undergoing in vitro fertilization: a randomized clinical study.

OBJECTIVE: To compare the efficacy of oral dydrogesterone with that of micronized vaginal P gel and micronized P capsule for luteal supplementation. DESIGN: Prospective, randomized clinical study. SETTING: Institute of Reproductive Medicine, Kolkata, India. PATIENT(S): A total of 1,373 infertile women undergoing IVF participated. INTERVENTION(S): Micronized P gel, P capsule, and oral dydrogesterone were administered for luteal support and compared. MAIN OUTCOME MEASURE(S): Demographic profile and pregnancy and miscarriage rates. RESULT(S): The overall pregnancy rate and miscarriage rate were comparable among the three groups. CONCLUSION(S): Oral dydrogesterone seems to be a promising drug for luteal support in woman undergoing IVF.