Cephalometric analysis of patients with beta thalassemia receiving fetal hemoglobin induction therapy

Objective

We aimed to determine the effects of fetal hemoglobin induction therapy in restricting or even reversing the cephalometric changes associated with beta thalassemia.

Materials and methods

In this comparative observational study, 90 participants were equally divided into three groups: a control group; patients with thalassemia major receiving blood transfusion (BT group); and patients receiving induction therapy (i.e., hydroxyl urea (5-10 mg/kg/day) or as much as 20 mg/kg/day) and thalidomide (2-10 mg/kg/day) along with blood transfusion (IT group). All patients underwent history taking and examination, photographic assessment, and radiographic evaluation with a lateral cephalogram. One-way ANOVA followed by post-hoc Tukey test was used to determine differences among groups.

Results

The IT group differed significantly from the BT group in all photographic and skull table parameters, and most cephalometric parameters, such as facial angle (p ≤ 0.001), middle and lower facial heights (p ≤ 0.001), and inter-incisal angle (p = 0.036); the mean values in the IT group were similar to those in the control group. In-addition, dental and soft tissue measurements significantly differed among groups. For most parameters, the mean difference indicated higher values in the BT group.

Conclusion

Induction therapy appeared to improve the facial angles, heights, and inter-incisal angles, whereas a class II skeletal pattern was observed in the transfusion only group. These findings suggest that fetal hemoglobin induction therapy might have restricted some of the cephalometric changes in patients with beta thalassemia.

A centromere-derived retroelement RNA localizes in cis and is a core element of the transcriptional landscape of Drosophila centromeres

Centromeres are essential chromosomal landmarks that dictate the point of attachment between chromosomes and spindle microtubules during cell division. The stable transmission of the centromere site through generations is ensured by a unique chromatin containing the histone H3 variant CENP-A. Previous studies have highlighted the impact of transcription on promoting CENP-A deposition. However, the specific sequences undergoing this transcription and their contribution to centromere function in metazoan systems remain elusive. In this study, we unveil the centromeric transcriptional landscape and explore its correlation with CENP-A in D. melanogaster, currently the only in vivo model with assembled centromeres. We find that the centromere-enriched retroelement G2/Jockey-3 (hereafter referred to as Jockey-3) is a major driver of centromere transcription, producing RNAs that localize to all mitotic centromeres, with the Y centromere showing the most transcription. Taking advantage of the polymorphism of Jockey-3, we show that these RNAs remain associated with their cognate DNA sequences in cis. Using a LacI/lacO system to generate de novo centromeres, we find that Jockey-3 transcripts do not localize to ectopic sites, suggesting they are unlikely to function as non-coding RNAs with a structural role at centromeres. At de novo centromeres on the lacO array, the presence of CENP-A augments the detection of exogenous lacO-derived transcripts specifically in metaphase. We propose that Jockey-3 contributes to the epigenetic maintenance of the centromere by promoting chromatin transcription, while inserting in a region that permits its continuous transmission. Given the conservation of retroelements as centromere components across taxa, our findings have broad implications in understanding this widespread association.

Auto-Correction for Inaccurate Auto-Segmentation of Abdominal MRI by Combining Deep Learning and Active Contour Method

PURPOSE/OBJECTIVE(S)

Fast and accurate auto-segmentation is crucial during the magnetic resonance-guided adaptive radiation therapy (MRgART). Deep learning auto-segmentation (DLAS) approaches do not always result in clinically acceptable contours, especially for complex abdominal organs. We have previously reported that the inaccurate DLAS for bowels can be refined using deep learning (DL) and/or active contour method (ACM). This study aims to develop an automatic contour correction (ACC) tool by combining DL and ACM techniques to correct for inaccurate DLAS of pancreas and stomach on MRI.

MATERIALS/METHODS

The ACC technique consisted of ACM and DL based on UNet system. The ACM utilizes the probability maps generated from DLAS models to establish 2D parameter maps and to initialize contour evolution, thus not requiring initial parameter adjustments. The Organ specific DL-UNet models were trained for pancreas and stomach contours obtained from a research DLAS tool on abdominal MRIs acquired during routine MRgART from a 1.5T MR-Linac using either turbo field-echo or balanced turbo field-echo sequences. The training dataset contained MR slices along with DLAS and ground truth contours from 54 abdominal MRL sets, and 540 additional augmented sets created by shifting and rotating. DLAS contours were classified based on the expected editing effort into the acceptable, minor edit, or major edit category using an in-house developed classification model. Performance of the obtained ACC models were tested on an independent dataset of 11 sets of abdominal MRIs.

RESULTS

For pancreas, the DL-UNet model improved 17% (26/153) and 2% (2/95) of the minor and major edits' slices of the testing dataset, respectively, to acceptable and 39% (37/95) of the major edit slices improved to minor edits. The ACM model improved 3% (4/153) of the minor edit slices to acceptable and the 36% (34/95) of the major slices to minor edits. Using the ACC technique with DL and ACM combined, the percentage of acceptable contours increased from 10% (29/277) to 24% (66/277), and minor edits from 55% (153/277) to 61% (170/277), while the percentage of the major edit slices reduced from 35% (95/277) to 15% (41/277). For stomach, the DL model improved 8% (29/366) of the minor edit slices to acceptable and 50% (16/32) of the major edit slices to minor edit slices. The ACM resulted in 2% (6/366) of minor edit slices to acceptable and 41% (13/32) of major edit slices to minor category. Combining both the DL and ACM, the overall percentage of acceptable stomach contours grew from 13% (58/456) to 22% (101/456) and the percentage of major edit slices reduced from 7% (32/456) to 2% (11/456).

CONCLUSION

The ACC method combining both DL and ACM models can substantially improve the quality of inaccurate DLAS contours of pancreas and stomach in a fully automated and fast manner, minimizing the subsequent manual editing time required for MRgART.

Clinical Usability-Oriented Automatic Contour Quality Evaluation for Deep Learning Auto-Segmentation

PURPOSE/OBJECTIVE(S)

Various auto-segmentations, including deep learning auto-segmentation (DLAS), are being increasingly adopted in radiotherapy. A common method to evaluate quality of auto-segmented contours uses thresholds of various quantitative metrics (e.g., dice similarity coefficient (DSC), mean distance to agreement (MDA), etc.) that are often averaged over all contour slices. This method fails to detect contour errors on individual slices, thus, does not reflect the current clinical practice (slice-by-slice evaluation) and the clinical usability (e.g., expected contour editing time). In addition, the use of multi-metrics is generally not easy to interpret. This work aims to develop a novel contour quality classification (CQC) model to evaluate auto-segmented contours based on their clinical applicability.

MATERIALS/METHODS

The CQC method was designed to classify a contour on a slice into acceptable, minor edit or major edit category, based on the expected editing effort/time. Organ-specific supervised ensemble tree classification models were trained to relate the slice-based quality category with the combination of seven commonly used calculatable quantitative metrics (i.e., DSC, MDA, Hausdorff 95% distance, surface DSC, added path length (APL), slice area and relative APL). The proposed method was demonstrated by training CQC models using DLAS contours of five abdominal organs (i.e., pancreas, duodenum, stomach, and small and large bowels) from 50 MRI sets and evaluating on 20 MRI and 9 CT testing sets. These test datasets were labelled by six individual observers and the consensus labels were generated through majority vote method. The model performance was evaluated using accuracy (acc), and risk rate (RR, the percentage of unacceptable slices mislabeled as acceptable) and compared with inter-observer variation and baseline threshold-based method.

RESULTS

Compared to the majority vote labels, the obtained CQC models achieved a mean accuracy of 95.8% ([94.5%-99.1%]) and 94.3% ([90.6%-96.9%]), and the mean RR of 0.8% ([0.3%-1.3%]) and 0.7% ([0%-1.1%]) for the MRI and CT testing sets, respectively. The CQC performance was comparable to the inter-observer variation and significantly higher than those from the threshold-based method with single or multiple metrics. The execution time on a typical abdominal dataset (e.g., 70 slices) took less than 3 seconds. Table 1 CQC models performance for different organs CONCLUSION: The proposed CQC model can classify the quality of a contour slice with high accuracy. This slice-based single-output evaluation method better reflects the current clinical practice and may be used to evaluate/compare performance of DLAS on any image modality, facilitating its clinical implementation and quality assurance.

Organ Specific Deep Learning-Based Correction of Inaccurate Auto-Segmentation on Abdominal MRI

PURPOSE/OBJECTIVE(S)

Deep learning-based automatic segmentation (DLAS) techniques offer limited success for abdominal organs on MRI, requiring substantial editing time. We have previously developed a deep learning based automatic contour correction (ACC) technique that can correct for inaccurate DLAS contours of bowels on MRI, reducing the manual editing time in MR-guided online adaptive radiation therapy (MRgART). This study aims to develop deep learning-based ACC models for pancreas and duodenum that are particularly difficult to contour either manually or with DLAS.

MATERIALS/METHODS

Dense UNet, a deep learning algorithm that combines UNet with dense blocks, was trained to create ACC models. Organ-specific models were trained for pancreas and duodenum contours obtained from a research DLAS tool on MRIs from a 1.5T MR-Linac. The training dataset contained MRI slices paired with DLAS contours from 54 abdominal MRL sets along with ground truth contours and 540 additional augmented sets created by shifting, rotating, and scaling each organ along with the contours and varying the noise and bias field for each patient set. Each DLAS contour was classified into the acceptable (no additional edits required), minor edit (only simple edits required), or major edit category based on the expected editing effort determined using a contour classification model developed in a separate study. The ACC models were trained for the slices requiring minor edit and major edit separately. Performance of the obtained models were tested using an independent 11 MRI sets in term of the change of contour category based on the contour classification model.

RESULTS

After applying the duodenum ACC model to the testing datasets, 16% (27/165) and 5% (8/178) of the minor and major edits' slices, respectively, improved to acceptable and 31% (54/178) of the major edit slices improved to minor edits. Furthermore, the total percentage of acceptable contours grew from 10% (36/378) to 19% (71/378) and the percentage of the major edit slices reduced from 47% (178/378) to 30% (115/378). After applying the pancreas ACC model to the testing datasets, 32% (47/143) and 1% (1/96) of the minor and major edits' slices, respectively, improved to acceptable and 49% (47/96) of the major edit slices improved to minor edit slices. Furthermore, the total percentage of acceptable contours grew from 14% (38/277) to 31% (86/277) and the percentage of major edit slices reduced from 35% (96/277) to 17% (48/277).

CONCLUSION

Deep learning based automatic contour corrections can substantially improve inaccurate DLAS contours of pancreas and duodenum on MRI that would otherwise require time-consuming edits, resulting in less manual intervention and increased efficiency during MRgART.

Patient-Specific Deep Learning Auto-Segmentation for MR-Guided Adaptive Radiotherapy of Prostate Cancer

PURPOSE/OBJECTIVE(S)

MR guided conventional prostate radiotherapy requires agreement of bladder and rectum position, size, and shape with reference plan anatomy. In many instances, treatments may be delayed to allow optimal filling of these organs at risk (OARs). To account for such changes, we propose a patient specific deep learning based auto-segmentation (PS-DLAS) solution to implement pseudo (adapt-to-shape) ATS planning as a replacement for (adapt-to-position) ATP for conventionally fractionated treatments, and to generate auto-segmented contours for ATS SBRT treatments.

MATERIALS/METHODS

A commercially available DLAS tool that allows to customize/re-train DLAS models to an individual patient was implemented and automated in 3 iterative steps: 1) training of initial PS-DLAS model based on the first daily MRI with manual contours for the patient, 2) using the trained model to auto-segment the next daily MRI (acquired at next fraction), and 3) re-training/updating the model with newly available daily MRI sets and the verified contours. Steps 1) and 3) were performed offline while the step 2 was online. The solution was tested using daily MRI sets collected for 6 prostate cancer patients treated with MR guided adaptive radiotherapy (MRgART) either in 5-fraction SBRT or conventionally fractionated with adapt-to-shape (ATS) or adapt-to-position (ATP) workflows on a 1.5T MR-Linac. The quality of the auto-segmented OAR contours, including bladder, penile bulb, prostate, rectum, and seminal vesicles, obtained in step 2) were assessed, where the acceptable contour slices (no editing required) were identified. Additionally, ATP was simulated using a pseudo ATS workflow on patient 6, allowing the PS-DLAS to be used on daily MRI sets and the obtained contours to be used in the daily plan optimization. The time saving and the plan quality of using the PS-DLAS were compared to those from the current standard clinical workflow.

RESULTS

The times for the offline model training in steps 1) or 3) were < 50 minutes and the times of applying the updated PS-DLAS to auto-segment a daily MRI set in step 2) were within 1 minute using a hardware of Intel(R) Xeon(R) Gold 5222 CPU. Among the auto-segmented contour slices, 87%, 92%, 88%, 86% and 84% were found to be acceptable for bladder, penile bulb, prostate, rectum, and seminal vesicles, respectively. The time savings of using the PS-DLAS were about 7 minutes, compared to the manual contouring in the current ATS workflow. Manual editing was almost always required in 1-3 most superior slices for each organ. For the patient tested with the pseudo ATS, time saving was 10 minutes with comparable plan quality to the standard ATP workflow.

CONCLUSION

In this study, we demonstrated use of a PS-DLAS approach to reduce contouring time in MR-guided ATS prostate radiotherapy. In addition, these results suggest a PS-DLAS approach combined with pseudo-ATS may reduce overall treatment times in conventionally fractionated MRgRT by eliminating time required for matching of OAR filling.

Phase III Study of Hypofractionated, Dose Escalation Radiotherapy vs. Conventional Pelvic Radiation Therapy followed by High Dose Rate Brachytherapy Boost for High Risk Adenocarcinoma of the Prostate (PCS VI): Acute Toxicity Results

PURPOSE/OBJECTIVE(S)

The low α\β ratio of 1.2-2 for prostate cancer (PCa) suggests high radiation-fraction sensitivity and predicts a therapeutic advantage of lager fraction size. We have recently shown (PCS5) that high risk prostate cancer patients can safely and effectively be treated with moderate hypofractionated radiation therapy (HF-RT). To date there has been no phase-III randomized clinical-trial comparing moderately HF-RT with EBRT and HDR boost (HDRB). We are reporting the acute safety of EBRT+HDRB compared to moderate HF-RT in this phase III Canadian trial.

MATERIALS/METHODS

From January 2015-June 2022, 308 high-risk localized PCa patients were randomized to receive either HF-RT or EBRT+HDRB. All patients received neo-adjuvant, concurrent, and long-term adjuvant androgen deprivation therapy (ADT). EBRT+HDRB consisted of 46 Gy in 2 Gy per fraction to the pelvis and a 15 Gy in one fraction HDR boost within 3 weeks of EBRT. HF-RT include concomitant dose escalation of 68 Gy in 2.72 Gy per fraction to the prostate, and 45 Gy in 1.8 Gy per fraction to the pelvic lymph-nodes.

RESULTS

Of the 308 patients, 148 received HF-RT and 144 EBRT+ HDRB. The remainder either withdrew from the study or were treated with standard (2 Gy per fraction) fractionation for technical reasons. In both intention to treat and as treated analysis, using log-Rank, there were more grade 1 or worse (G1+) acute GI and GU events and more G2+ acute GI events in the HF-RT than EBRT+HDRB. As treated analysis the acute G1+ and G2+ GI events were 92 vs 77 (60.1% vs. 53.5%; p < 0.017) and 21 vs 10 (13.7% vs. 6.9%; p = 0.052), respectively for HF-RT and EBRT + HDRB. Similarly, the G1+ acute GU events were 123 vs. 101 (80.4% vs.70.1%; p < 0.001) respectively for HF-RT and EBRT+HDRB. There were only four G3 GI and one G3 GU acute toxicities in both arms. No grade 4 toxicities were reported.

CONCLUSION

This is the first study of EBRT+HDRB compared to moderate HF dose escalated RT in high-risk prostate cancer patients treated with long-term ADT and pelvic RT. Our results demonstrate that both treatment approaches are well-tolerated and that EBRT+HDRB carries less G2+ GI and G1+ GU acute toxicities.

Evaluation of the Effect of Suprachoroidal Triamcinolone Injection on Refractory Diabetic Macular Edema

Purpose:  To assess the safety and efficacy of supra choroidal triamcinolone injection (SCT) in cases of refractory diabetic macular oedema. Study Design:  Interventional case series. Place and Duration of Study:  Lahore General Hospital, Lahore, from July to December 2019. Methods:  A total of 22 eyes of patients above 18 years of age, with either Type-1 or Type-2 diabetes mellitus and treatment resistant central Diabetic Macular Edema (DME) of 320 um or more (measured on Zeiss Cirrus HD-OCT) and Best Corrected Visual Acuity (BCVA) of less than or equal to 20/40 were included in the study. BCVA, Intra Ocular Pressure (IOP) and Central Subfield Thickness (CST) was recorded. After Supra-Choroidal triamcinolone (SCTA), patients were followed up at one and three months and same clinical parameters were recorded and the results were analysed. Results:  out of 22 patients, 10 (45.45%) were males and 12 (54.54%) were females. Mean pre injection CST was 615.5 ± 200.28 um and Log MAR BCVA was 0.9 ± 0.20. Mean post injection CST at one and three months was 302.45 ± 52.45 and 301.66 ± 55.82 um. Mean post injection Log MAR BCVA at one and three months was 0.52 ± 0.3 and 0.40 ± 0.22. The results were statistically significant for pre and post injection CST at both one and three months (p-value < 0.00001). Pre and post injection BCVA was also statistically significant (p-value < 0.05). Conclusion:  CST is a safe and effective means to reduce refractory diabetic macular edema and improve OCT macular thickness. Key Words:  Diabetic macular edema, Suprachoroidal Triamcinolone, Central sub-field thickness.

Comparison between intracameral ceftazidime & cefuroxime for the prevention of post-operative endophthalmitis

Purpose: To compare the efficacy and safety of intracameral injection of ceftazidime and cefuroxime during cataract surgery for the prevention of post-operative endophthalmitis. Study Design: Quasi experimental study. Place and Duration of Study: Ophthalmology Department Unit-1, Lahore General Hospital, Lahore. From November 2016 to March 2018. Material and Methods: Patients undergoing cataract surgery in the eye department were selected by convenient sampling to receive prophylactic intracameral antibiotics at the end of routine cataract surgery. Patients were divided into two groups; group A received Cefuroxime and group B received Ceftazidime both as 1 mg /0.1ml intracameral injections at the end of the surgery after wound hydration and before chamber formation. The patients were examined pre and post operatively on slit lamp and the number of cells in the anterior chamber (A/C) were counted on day 1, week 1 and week 6 after surgery. Results: Out of total 260 patients 130 were allocated to each group. On first post-operative day in Group A, A/C cell counts were grade 1 in 22 patients, grade 2 in 93 patients, grade 3 in 14 patients and Grade 4 in 1 patient. In group B, A/C cell counts were grade 1 in 11 patients, grade 2 in 96 patients, grade 3 in 20 patients and grade 4 in 3 patients. After one week, in group A, cell counts were grade 0 in 27 patients, grade 1 in 100 patients, grade 2 in 2 patients and grade 4 in 1 patient. While in group B, A/C cell counts were Grade 0 in 23 patients, grade 1 in 102 patients, grade 2 in 3 patients and grade 3 in 2 patients. After 6 weeks no patient in any group showed any activity in the anterior chamber. Conclusion: There is little difference in efficacy of intracameral ceftazidime antibiotic prophylaxis as compared to the intracameral cefuroxime.

Changes in Intra Ocular Pressure after Phacoemulsification with intraocular lens implantation

Objectives: To study the changes in intra ocular pressure (IOP) after phacoemulsification with intra ocular lens implantation. Study Design: Quasi experimental study. Setting: Lahore General Hospital, Lahore. Period: From 1-3-2017 to 30-9-2018. Material & Methods: The study was conducted after getting approval from ‘’Ethical Review Board’’ of Lahore General Hospital, Lahore. Patients presenting to the Eye OPD Lahore General Hospital were assessed for inclusion and exclusion criteria. All patients (n=) diagnosed with cataract significantly decreasing vision were selected. Patients having any coexisting ocular pathology i.e. corneal opacity, Glaucoma etc. were excluded from study. Pre operatively intraocular pressure was measured on slit lamp with the applanation tonometry. The patients were called for follow-up after at which time Visual acuity and intraocular pressure was again measured and findings recorded in the proforma. All the surgeries were performed by single surgeon. Results: Pre-operative mean intra ocular pressure was 22.02±1.403 mm Hg which reduced significantly to 20.39±1.363mm Hg, 19.61±1.418mm Hg, 18.94±1.738mm Hg after one day, one week and one month of surgery respectively. Conclusion: Phacoemulsification cataract surgery leads to a decrease in intraocular pressure.

Retinal Ganglion Cell Complex Changes after Intravitreal Bevacizumab for Diabetic Macular Edema

OBJECTIVE

To measure the changes in ganglion cell complex as measured on optical coherence tomography (OCT) after intravitreal injection of bevacizumab (Avastin).

STUDY DESIGN

Quasi-experimental study.

PLACE AND DURATION OF STUDY

Department of Ophthalmology, Lahore General Hospital, Lahore, from March 2017 to April 2018.

METHODOLOGY

Patients presenting to the Eye OPD were assessed for diabetic macula edema requiring intravitreal injection of anti-VEGF. Patients having any coexisting ocular pathology hindering the OCT measurement, i.e. corneal opacity, vitreous hemorrhage, retinal detachment or having macular edema secondary to other cases were excluded. Retinal ganglion cell complex thickness and signal strength was measured in superior, inferior, supero-nasal, superotemporal, infero-nasal and infero-temporal quadrants on OCT. Pre-injection, visual acuity was measured, OCT performed and the findings were recorded on a designed proforma. Post-injection, the patients were called for follow-up after one month at which time same measurements were evaluated. All the injections were administered by a single surgeon.

RESULTS

The thickness of ganglion cell complex increased significantly (p <0.001) one month after Intravitreal injection of bevacizumab.

CONCLUSION

Intravitreal administration of bevacizumab in diabetic macular edema affects the measurement of retinal ganglion cell complex thickness on OCT.

Retinal Nerve Fiber Layer Thickness Changes after Phacoemulsification with Intraocular Lens Implantation

OBJECTIVE

To determine the changes in the retinal nerve fiber layer as measured on optical coherence tomography (OCT) after phacoemulsification with intraocular lens implantation.

STUDY DESIGN

Quasi-experimental study.

PLACE AND DURATION OF STUDY

Department of Ophthalmology, Lahore General Hospital, Lahore, from March 2017 to April 2018.

METHODOLOGY

All patients (n=64) diagnosed with cataract and requiring surgery were included in study. Patients having any coexisting ocular pathology hindering the OCT measurement, i.e. corneal opacity, vitreous hemorrhage, retinal detachment etc. were excluded from study. Retinal nerve fiber layer thickness was measured in four quadrants. Signal strength on OCT was also documented. Preoperatively, visual acuity was measured, OCT performed, and the findings were recorded on a designed proforma. Postoperatively, the patients followed up after one month for visual acuity and OCT measurement.

RESULTS

Retinal nerve fiber layer thickness was increased postoperatively as measured on OCT as did the signal strength. The increase was statistically significant (p <0.001).

CONCLUSION

Removal of cataract by surgery enhances the OCT measurement of retinal nerve fiber layer, resulting in increased thickness.

Tm(iii) complexes undergoing slow relaxation of magnetization: exchange coupling and aging effects

The present study focuses on the dynamic magnetic behaviour of exchange coupled 3d-4f complexes containing the scarcely investigated non-Kramers Tm³⁺ center, the 3d metal ions being either the low-spin Fe³⁺ (1) or the diamagnetic Co³⁺ (2) ion. Both complexes display field-induced slow relaxation of magnetization. The field and temperature dependences of the relaxation rate provided indication of relevant contributions from quantum tunnelling, direct, Orbach and Raman processes, with only minor effects from exchange coupling interactions. Furthermore, the aged sample of 2 exhibited an additional relaxation process, possibly due to structural modifications accompanied by solvent loss, highlighting the importance of a careful consideration of this factor when analysing the magnetization dynamics in solvated systems.

Modified Hodge test: A simple and effective test for detection of carbapenemase production

BACKGROUND AND OBJECTIVES

Resistance among bacterial isolates is the leading cause of increased mortality and morbidity worldwide. Carbapenems once thought to be effective are becoming ineffective mostly due to the emergence of carbapenemase. This study was designed to determine in vitro efficacy of Modified Hodge test for detection of carbapenemase production in Gram negative rods.

MATERIAL AND METHODS

The study was done in the Department of Microbiology, Armed Forces Institute of Pathology Rawalpindi Pakistan from January 2010 to December 2010. A total of 200 Gram negative rods from different clinical samples were taken. Those isolates which showed intermediate or susceptible zones i.e 16mm-21mm on disc diffusion were included in the study. These isolates were then subjected to Modified Hodge test.

RESULT

Out of 200 isolates, 138 (69%) were positive for carbapenemase production by Modified Hodge test. Out of 138 MHT positive organisms, the frequency of E. coli was 38%, followed by Pseudomonas aeruginosa (30%), Klebsiella pneumoniae (17%), Acinetobacter baumannii (12%), Citrobacter diversus (2%) and Enterobacter agglomerans (1.4%).

CONCLUSION

Modified Hodge test is a simple test which can be performed in the routine lab for detection of carbapenemases in isolates showing intermediate or sensitive zone diameter on disc diffusion.