Retromaxillary Cell: Radiological Analysis and Clinical Significance.

Retromaxillary cell (RMC), one of the anatomical variations of the posterior ethmoidal cell and is often overlooked during primary functional endoscopic sinus surgery. The incomplete removal of the disease from RMC leads to need for revision surgery. This study was aimed at analyzing Computed tomography scans of patients' paranasal sinuses for the incidence, types and radiological evaluation of Retromaxillary cell. Incidence of RMC was 74% (74/100 sides). The sex distribution was 31 (62%) males and 19 (38%) females. 34 patients (85%) had bilateral RMC and 6 patients (15%) had unilateral RMC. Lateral extension of RMC ranged from 1.03 to 11.3 mm. Out of 74 sides examined, 20 (27.02) were type I, 36 were type II (48.64%) and 18 (24.32%) were type III. The incidence of maxillary sinus disease on RMC sides and non-RMC sides has no significant difference (p < 0.5). RMC is lateral extension of posterior ethmoidal cell beneath the orbit and posterosuperior to maxillary sinus. The depth of the RMC is highly variable. The risk of residual disease in FESS is high in Type III RMC and one should pay attention to presence or absence of RMC and type of RMC prior to the endoscopic sinus surgery. Radiological study of RMC helps in preoperative planning and therefore intraoperative complete clearance of disease in endoscopic sinus surgery.

A Highly Reliable Convolutional Neural Network Based Soft Tissue Sarcoma Metastasis Detection from Chest X-ray Images: A Retrospective Cohort Study.

INTRODUCTION: soft tissue sarcomas are a subset of malignant tumors that are relatively rare and make up 1% of all malignant tumors in adulthood. Due to the rarity of these tumors, there are significant differences in quality in the diagnosis and treatment of these tumors. One paramount aspect is the diagnosis of hematogenous metastases in the lungs. Guidelines recommend routine lung imaging by means of X-rays. With the ever advancing AI-based diagnostic support, there has so far been no implementation for sarcomas. The aim of the study was to utilize AI to obtain analyzes regarding metastasis on lung X-rays in the most possible sensitive and specific manner in sarcoma patients. METHODS: a Python script was created and trained using a set of lung X-rays with sarcoma metastases from a high-volume German-speaking sarcoma center. 26 patients with lung metastasis were included. For all patients chest X-ray with corresponding lung CT scans, and histological biopsies were available. The number of trainable images were expanded to 600. In order to evaluate the biological sensitivity and specificity, the script was tested on lung X-rays with a lung CT as control. RESULTS: in this study we present a new type of convolutional neural network-based system with a precision of 71.2%, specificity of 90.5%, sensitivity of 94%, recall of 94% and accuracy of 91.2%. A good detection of even small findings was determined. DISCUSSION: the created script establishes the option to check lung X-rays for metastases at a safe level, especially given this rare tumor entity.

A novel semi-supervised framework for UAV based crop/weed classification.

Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agriculture (PA). In this regard, targeted weed control will be a critical component significantly helping in achieving the goal. A prerequisite for such control is a robust classification system that could accurately identify weed crops in a field. In this regard, Unmanned Aerial Vehicles (UAVs) can acquire high-resolution images providing detailed information for the distribution of weeds and offers a cost-efficient solution. Most of the established classification systems deploying UAV imagery are supervised, relying on image labels. However, this is a time-consuming and tedious task. In this study, the development of an optimized semi-supervised learning approach is proposed, offering a semi-supervised generative adversarial network for crops and weeds classification at early growth stage. The proposed algorithm consists of a generator that provides extra training data for the discriminator, which distinguishes weeds and crops using a small number of image labels. The proposed system was evaluated extensively on the Red Green Blue (RGB) images obtained by a quadcopter in two different croplands (pea and strawberry). The method achieved an average accuracy of 90% when 80% of training data was unlabeled. The proposed system was compared with several standards supervised learning classifiers and the results demonstrated that this technique could be applied for challenging tasks of crops and weeds classification, mainly when the labeled samples are small at less training time.

A promising strain of Streptomyces sp. with agricultural traits for growth promotion and disease management.

A bacterial strain, Streptomyces sp. CIMAP- A1 was isolated from Geranium rhizosphere and identified by morphological, physiological, biochemical and molecular characters (16S rDNA gene sequence). Phylogenetically, it was found most closely related to S. vinacendrappus, strain NRRL-2363 with 99% sequence similarity. The strain had potential antagonistic activity (in vitro) against wide range of phytopathogenic fungi like Stemphylium sp., Botrytis cinerea, Sclerotinia sclerotiorum, Colletotrichum spp., Curvularia spp., Corynespora cassicola and Thielavia basicola. The extracellular secondary metabolites produced by the strain in the culture filtrates significantly inhibited the spore germination, growth of germ tube of the germinated spores and radial growth of Alternaria alternata, Colletotrichum acutatum, Curvularia andropogonis and Fusarium moniliforme. The extraction of culture filtrate with solvents and purification by following VLC and PTLC methods always yielded a 10th fraction antifungal compound showing activity against wide range of phytopathogenic fungi. The strain was able to produce siderophores and indole-3-acetic acid. The strain was found to enhance the growth and biomass production of Geranium. It increased 11.3% fresh shoot biomass of Geranium and 21.7% essential oil yield.

Production, purification, and characterization of antifungal metabolite from Pseudomonas aeruginosa SD12, a new strain obtained from tannery waste polluted soil.

A new strain, SD12, was isolated from tannery waste polluted soil and identified as Pseudomonas aeruginosa on the basis of phenotypic traits and by comparison of 16S rRNA sequences. This bacterium exhibited broad-spectrum antagonistic activity against phytopathogenic fungi. The strain produced phosphatases, cellulases, proteases, pectinases, and HCN and also retained its ability to produce hydroxamate-type siderophore. A bioactive metabolite was isolated from P. aeruginosa SD12 and was characterized as 1-hydroxyphenazine ((1-OH-PHZ) by nuclear magnetic resonance (NMR) spectral analysis. The strain was used as a biocontrol agent against root rot and wilt disease of pyrethrum caused by Rhizoctonia solani. The stain is also reported to increase the growth and biomass of Plantago ovata. The purified compound, 1-hydroxyphenazine, also showed broad-spectrum antagonistic activity towards a range of phytopathogenic fungi, which is the first report of its kind.