Performance evaluation of Gallium-68 radiopharmaceuticals production using liquid target PETtrace 800 cyclotron

Due to increased demand, cyclotron has an expanding role in producing Gallium-68 (68Ga) radiopharmaceuticals using solid and liquid targets. Though the liquid target produces lower end-of-bombardment activity compared to the solid target, our study presents the performance of 68Ga radiopharmaceuticals production using the liquid target by evaluating the end-of-bombardment activity and the end-of-purification activity of [68Ga]GaCl3. We also present the effect of increasing irradiation time, which significantly improves the end-of-synthesis yield. From the result obtained, the end-of-bombardment activity produced was 4.48 GBq, and the [68Ga]GaCl3 end-of-purification activity produced was 2.51 GBq with below-limit metallic impurities. Increasing the irradiation time showed a significant increase in the end-of-synthesis activity from 1.33 GBq to 1.95 GBq for [68Ga]Ga-PSMA-11 and from 1.13 GBq to 1.74 GBq for [68Ga]Ga-DOTA-TATE. Based on the improvements made, the liquid target production of 68Ga radiopharmaceuticals is feasible and reproducible to accommodate up to 5 patients per production. In addition, this work also discusses the issues encountered, together with the possible corrective and preventative measures.

Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians

BACKGROUND

Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population.

OBJECTIVE

To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores.

METHODS

We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined.

RESULTS

Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration.

CONCLUSIONS

In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.

Electrochemical quantification of atrazine-fulvic acid and removal through bismuth tungstate photocatalytic hybrid membranes

Herbicides such as atrazine and humus substances such as fulvic acid are widely used in agricultural sector. They can be traced in surface and groundwater around the agriculture field at concentrations beyond the approved limit due to their mobility and persistence. Bismuth-based photocatalysts activated by visible light are potential materials for removing various organic pollutants from water bodies. These photocatalysts can also be suitable candidates for developing a hybrid membrane with anti-fouling properties. In this study, Bi2WO6 nanoparticles were synthesized via the hydrothermal method and integrated into the cellulose acetate (CA), polyetherimide (PEI), polysulfone (PSF) and polyvinylidene fluoride (PVDF) polymers via physical blending approach. The hybrid membranes were then characterized by FTIR, XPS and FESEM to confirm the chemical bonding, chemical composition and surface morphology of Bi2WO6. Thus, the pure water flux of CA (35.6 L m-2 h-1), PEI (46.56 L m-2 h-1), PSF (6.84 L m-2 h-1), and PVDF (68.47 L m-2 h-1) hybrid membranes has significantly enhanced than the pristine CA, PEI, PSF and PVDF membranes. The significant rejection of atrazine-fulvic acid was observed with hybrid membranes in the order of CA (84.1%) > PVDF (72.7%) > PEI (47.8%) > PSF (37.2%), and these membranes have shown an excellent flux recovery ratio than pristine membranes. Further, electrochemical quantification studies were performed to analyze the removal efficiency of atrazine-fulvic acid from water. In this present work, GO-modified SPE was employed for electrochemical sensing studies. The resultant CA hybrid membrane achieved removal efficiency of 84.08% for atrazine. It was observed that the Bi2WO6 established strong bonding with CA, and PVDF membranes, thus showing a significant removal efficiency and FRR than other hybrid and pristine membranes.

In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm

BACKGROUND

Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients.

OBJECTIVE

To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score.

METHODS

The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score.

RESULTS

A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation.

CONCLUSIONS

ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.

In-hospital risk stratification algorithm of Asian elderly patients

Limited research has been conducted in Asian elderly patients (aged 65 years and above) for in-hospital mortality prediction after an ST-segment elevation myocardial infarction (STEMI) using Deep Learning (DL) and Machine Learning (ML). We used DL and ML to predict in-hospital mortality in Asian elderly STEMI patients and compared it to a conventional risk score for myocardial infraction outcomes. Malaysia's National Cardiovascular Disease Registry comprises an ethnically diverse Asian elderly population (3991 patients). 50 variables helped in establishing the in-hospital death prediction model. The TIMI score was used to predict mortality using DL and feature selection methods from ML algorithms. The main performance metric was the area under the receiver operating characteristic curve (AUC). The DL and ML model constructed using ML feature selection outperforms the conventional risk scoring score, TIMI (AUC 0.75). DL built from ML features (AUC ranging from 0.93 to 0.95) outscored DL built from all features (AUC 0.93). The TIMI score underestimates mortality in the elderly. TIMI predicts 18.4% higher mortality than the DL algorithm (44.7%). All ML feature selection algorithms identify age, fasting blood glucose, heart rate, Killip class, oral hypoglycemic agent, systolic blood pressure, and total cholesterol as common predictors of mortality in the elderly. In a multi-ethnic population, DL outperformed the TIMI risk score in classifying elderly STEMI patients. ML improves death prediction by identifying separate characteristics in older Asian populations. Continuous testing and validation will improve future risk classification, management, and results.

Comparison of ELISA and PCR Assays for Detection of Pork Adulteration in Halal-Labelled Beef Products

Background: Food adulteration with pork in processed beef products is one of the most serious issues in a food sector in a Muslim-majority country since it is related to religious food ethics regarding the halal products. The goal of this research is to test the suitability of ingredients in beef floss and its Halal by knowing the presence of porkDNA and  protein in those products. Methods: Meat products were prepared from two famous marketplaces in Indonesia labeled contain beef meat. In this study, a qualitative Enzyme-Linked Immunosorbent Assay (ELISA) test was compared to a conventional Polymerase Chain Reaction (PCR) assay to determine pork adulteration in beef floss. Results: The results of the ELISA test showed that two products labeling Halal and containing beef ingredients were positive for pork. Those two samples continued testing using conventional PCR assay. The result of the conventional PCR assay was negative for those two samples. Conclusion: It may be helpful to utilize both traditional PCR and ELISA for species detection due to the possibly inhibiting compounds contained in some processed meat products. The results of this research suggest that ELISA is better than conventional PCR method for product samples that have received an intensive heating process.

Environmental sustainability assessment of wooden furniture produced in Pakistan

Life cycle assessment was carried out for a conventional wooden furniture set produced in Mardan division of the Khyber Pakhtunkhwa province of Pakistan during 2018-19. Primary data regarding inputs and outputs were collected through questionnaire surveys from 100 conventional wooden furniture set manufacturers, 50 in district Mardan and 50 in district Swabi. In the present study, cradle-to-gate life cycle assessment approach was applied for a functional unit of one conventional wooden furniture set. Production weighted average data were modelled in the environmental impacts modelling software i.e., SimaPro v.8.5. The results showed that textile used in sofa set, wood preservative for polishing and preventing insects attack and petrol used in generator had the highest contribution to all the environmental impact categories evaluated. Total cumulative energy demand for wooden furniture set manufactured was 30,005 MJ with most of the energy acquired from non-renewable fossil fuel resources.

Pleistocene paleodrainages explain the phylogeographic structure of Malaysian populations of Asian arowana better than their chromatic variation

Little is known about the genetic structure and phylogeography of Asian arowana (Scleropages spp.). Natural variation in body color has led to the informal distinction of chromatic varieties, but previous studies that attempted to genetically characterize these varieties did not comprehensively cover their geographical distribution. In Malaysia, about 10 drainage-restricted populations of Asian arowana are known that are currently classified into 2 species and 3 color varieties. In this study, we used 3 molecular markers to test 2 hypotheses explaining the relationships among 9 of these populations. The first hypothesis postulates that each color variety forms a monophyletic group, whereas the second hypothesis assumes that Pleistocene paleodrainages shaped the distribution of these populations. We found that the overall genetic variability is low within Asian arowana and that the green variety is non-monophyletic, with other varieties nested within. Instead, the populations of Malaysia belong to 3 genetic lineages that are allopatrically distributed. The ages and distribution of 2 of these lineages are consistent with past connections through paleodrainages, whereas the last lineage is restricted to Central Sarawak. Overall, our results reject the first hypothesis, demonstrating that the geographic origin of specimens is a better phylogenetic indicator than their body color. This study highlights the importance of Malaysia in the conservation of Asian arowana, because it is the only country in which populations of all 3 main genetic lineages occur.

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

Background

Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific.

Objective

Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score.

Methods

The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction.

Results

Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation.

Conclusions

In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

Potential application of biomass derived graphene for COVID-19 pandemic

Since the emergence of the novel coronavirus disease (COVID-19) pandemic, intense research has been carried out to find the effective vaccine. However, this issue remains as a global challenge. Graphene has captured various attention due to promising antimicrobial and antiviral applications, hydrophobic characteristic and superior electrical conductivity. Recently, biomass derived graphene also promises great opportunity to combat the spread COVID-19. In this paper, we demonstrated the ability and role of biomass derived graphene as superhydrophobic coating, biosensors and disinfectant in the fight against COVID-19.

Effect of polyphenols on activated sludge biomass during the treatment of highly diluted olive mill wastewaters: biomass dynamics and purifying performances

This study aims to investigate the feasibility of treating olive mill waste water (OMWW) by activated sludge pilot (AS) after its high dilution (1%) by urban waste water (UWW) and to study the effect of polyphenol compounds on the biomass during the treatment. Specific oxygen uptake rate (SOUR), mixed liquor volatile suspended solids (MLVSS), chemical oxygen demand (COD) and total polyphenols, were followed up over 100 days. In spite of the polyphenols' high concentration (up to 128 mg·L^-1), successful biomass growth of 7.12 g _MLVSS.L ^-1 and activity were achieved. Most of the bacteria (Pseudomonas sp., Klebsiella oxytoca, Citrobacter fereundii, Escherichia coli and Staphylococcus sp.) and fungi (Trichoderma sp., Rhizopus sp., Aspergillus niger, Penicillium sp., Fusarium sp., Alternaria) identified in the aerobic basin during the stabilization stage were known to be resistant to OMWW and showed effective adaptation of the biomass to polyphenols in high concentration. COD and polyphenols were highly eliminated (90%, 92% respectively). The sludge volume index in the pilot settling tank was almost constant at around 120 mL.g ^-1. This suggests the possibility of managing OMWW by simple injection at a given percentage in already functioning conventional AS treating UWW.

Determining hypertensive patients' beliefs towards medication and associations with medication adherence using machine learning methods

Background

This study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients' adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients' adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature.

Methods

Data from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.

Result

Machine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.

Conclusion

This study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.

Inhibiting effect of textile wastewater on the activity of sludge from the biological treatment process of the activated sludge plant

Textile industry represents an important source of toxic substances rejected in environment. Indeed, effluent of these industries contains dyes and chemicals. They are rejected in environment without any treatment. The aim of this work is to evaluate ecotoxicological effect of industrial textile effluents on the sludge harvested from activated sludge treatment plant of Marrakech city (Morocco). For this, we are interested in determining the inhibition condition that corresponds to 50% decrease of bacterial activity in sludge. Obtained results showed that inhibition percentage of bacterial activity depends narrowly on contact time and on added effluent volume, until a limit concentration where there is no degradation of substratum. In fact, substratum degradation speed shows about 65 times decrease when 80% (v/v) of textile wastewater is added, in comparison with the controlled one. Consequently the inhibition constant (Ki) that corresponds to 50% of bacterial inhibition activity is estimated to 0.65 mg l^-1 of dye. These studies confirm a real ecotoxicological risk of these effluents. Therefore, a treatment is mandatory before their rejection in environment.

Prevalence and factors associated with infectious intestinal diseases in Ras Al Khaimah, United Arab Emirates, 2017: A population-based cross-sectional study

BACKGROUND

The United Arab Emirates (UAE) is a rapidly developing high-income country that has experienced significant population growth, urbanization, and improvements in the standard of living since its formation in 1971. Published estimates on the prevalence of infectious intestinal diseases (IID) in the UAE are scarce and exclusively based on hospital data. The aim of this study was to provide the first prevalence estimates of IID in the UAE.

METHODS

A population-based cross-sectional study design using a telephone-based questionnaire was used to estimate the IID prevalence in the previous 4 weeks in a representative sample of the Ras Al Khaimah (RAK) population from January to September 2017.

RESULTS

Data were collected from 1254 participants (57.3% male; 25.2% <18 years). The prevalence of IID was 4.2% in the 4 weeks prior to the interview. Multivariate logistic regression analysis identified that being female (odds ratio (OR) 2.4, 95% confidence interval (CI) 1.2-5.1) and having a middle-range monthly household income (approx. USD 4080-<6800: OR 5.42, 95% CI 1.15-25.48; approx. USD 6800-<9530: OR 7.13, 95% CI 1.47-34.57) were positively associated with IID. Age ≥6 years was negatively associated with IID (OR 0.95, 95% CI 0.90-0.99). Forty-nine percent of participants with an IID sought medical care and 20.8% took over-the-counter medication.

CONCLUSIONS

This study provides the first population-based prevalence estimates of IID in the UAE, which are similar to those reported in China (4%), but lower than those reported in Canada (10%), the Netherlands (7%), and the USA (6%).

Very Early Cytomegalovirus Infection After Renal Transplantation: A Single-Center 20-Year Perspective

Background:

Cytomegalovirus (CMV) infection risk in the first month after transplantation is felt to be minimal; however, the epidemiology has not been specifically investigated, particularly in the modern era of potent immunosuppressive regimens and universal CMV prophylaxis.

Objective:

The aim of this study was to describe the incidence of and risk factors associated with CMV occurring less than 30 days after transplant and evaluate the effect of very early CMV on outcomes.

Methods:

Retrospective, single-center study of adult renal transplant (RTX) recipients between January 1, 1994 and December 31, 2014.

Results:

A total of 5225 patients who received a renal transplant in the study time period were reviewed for the presence of CMV infection occurring less than 30 days after transplant. Of these, only 14 patients demonstrated this finding for an overall incidence of 0.27%. Half of these patients were considered to be at heightened risk due to being a recipient of a non-primary transplant or on chronic immunosuppression. This left seven patients without known risk factors for very early CMV to evaluate. In this group, time from transplant to CMV infection was 13.5 ± 7 days. The majority (57.1%, n = 4) were high-risk serostatus (CMV D+/R−) and occurred in the valganciclovir era (71.4%, n = 5). Lymphocyte-depleting induction predominated (57.1%, n = 4). Average cold ischemic time (CIT) was 19.7 ± 7.7 hours. Three patients had post-operative complications, two required exploratory-laparotomy for hemorrhage. When evaluating outcomes, 43% (n = 3) had subsequent episodes of CMV infection, 28.6% (n = 2) developed rejection, and 28.6% (n = 2) died. Outcomes between patients with CMV infection less than 30 days and those with CMV infection more than 30 days after transplant were not significantly different.

Conclusions:

In our review of over 5000 kidney transplants, the incidence of CMV infection in the first 30 days after renal transplant is 0.2%. Notable common patient characteristics include hemorrhage requiring re-operation and prolonged CIT. Outcomes were similar to CMV occurring more than 30 days after transplant. This study should provide the clinician with some reassurance; despite potent immunosuppressive therapy, CMV infection in the first 30 days is unlikely.

Nail-Patella Syndrome: A Rare Cause of Nephrotic Syndrome in Pregnancy

Nail–Patella syndrome (NPS) is an inherited disease with characteristic nail, limb, and renal anomalies. While almost all patients manifest classical skeletal and nail abnormalities, renal involvement is seen in only 40% of patients. Asymptomatic proteinuria is the most common renal presentation. Although a substantial number of patients can progress to nephrotic range proteinuria, development of end-stage kidney disease is rare. Pathological abnormalities are appreciable only by electron microscopy. Our patient who had not been diagnosed with NPS previously presented with new-onset proteinuria during the second trimester of her first pregnancy. The characteristic physical and radiological features confirmed the diagnosis of NPS. Her clinical course was complicated by nephrotic syndrome with fetal loss at 22 weeks and postpartum renal vein thrombosis. Nephrotic state improved after termination of pregnancy. She was managed with angiotensin receptor blocker and anticoagulation. At the end of 2 years of follow-up, she has normal glomerular filtration rate with minimal proteinuria.

A surge in anti-dsDNA titer predicts a severe lupus flare within six months

OBJECTIVE

Rising anti-double-stranded (ds) DNA titers have been shown by some, but not all, studies to be predictive of disease flares in systemic lupus erythematosus (SLE). We hypothesized that a rapid and substantial rise in anti-dsDNA titer (anti-dsDNA surge) would be a good predictor of a clinically important SLE flare.

METHODS

A matched case-control study was conducted in an academic rheumatology practice setting. Our primary endpoint was the occurrence of a severe SELENA-SLEDAI (SS) flare within six months of an anti-dsDNA surge, and secondary endpoints were mild/moderate SS flares, as well as BILAG A and B renal flares. Cases were identified as those patients whose disease course included a surge of anti-dsDNA, defined as an increase of anti-dsDNA titer by the Crithidia luciliae immunofluorescence (CLIF) assay from 0 to 3+/4+, or from 1+ to 4+, within a period of less than 12 months. The date of the anti-dsDNA surge was defined as Day 0. Two control SLE patients were identified for each case and were matched for age, sex, race, and visit date closest to case Day 0, but without an anti-dsDNA surge. Logistic regression models were used to detect associations between anti-dsDNA surges and severe SS flares.

RESULT

A higher proportion of cases, compared to controls, experienced a severe SS flare within six months of Day 0 (OR 6.3 (95% confidence intervals 2.0-19.9), p = 0.02). Associations with all flares and hospitalizations for flares were also observed. However, an anti-dsDNA surge was not predictive of a renal flare.

CONCLUSION

An anti-dsDNA surge predicts the subsequent development of a severe SS flare within six months. Physicians should closely monitor such patients and treat promptly at the first sign of clinical activity.

Aberrant pathway in an anomaly: pre-excitation syndrome in association with coronary sinus aneurysm

Congenital coronary sinus anomalies are unusual and they rarely coexist with accessory atrio-ventricular pathways. These anomalies are generally symptomatic; however they can cause difficulty in mapping. The association between accessory pathway and coronary sinus anomalies may suggest an embryological link. Here, we report on a male patient with an accessory conducting pathway in the coronary sinus, leading to Wolff-Parkinson-White (WPW) syndrome.

Coeur en sabot

In tetralogy of Fallot (TOF), the most common form of cyanotic congenital heart disease, only a few patients reach adulthood without surgical correction. We present a case of a woman with TOF who was diagnosed at the age of 39 when she presented with features of congestive heart failure. The main factor contributing to her longevity included the slow development of her pulmonary artery stenosis together with left ventricular hypertrophy. Less than 3% of all patients with uncorrected TOF survive beyond their 40s but late operative repair is still a valuable option. This case provides an insight into the late outcome of an older patient with uncorrected TOF.

Development and Validation of a RP-HPLC Method for Determination of Cyclosporine in Capsule

A simple, specific and accurate reverse phase high performance liquid chromatographic method was developed for the determination of cyclosporine in capsule dosage form. XTerra C18 column was used as stationary phase with mobile phase acetonitrile in combination with 0.1% trifluoro acetic acid buffer and pH is adjusted to 1.4. Method was developed in an isocratic run of 20% trifluoro acetic acid with 80% acetonitrile for 10 min, at flow rate of 1 ml/min. Effluents were monitored at 210 nm. Retention time of cyclosporine was 3.855 min. The method was validated for specificity, linearity, accuracy, precision, limit of quantification, limit of detection, robustness and solution stability. Limit of quantification and limit of detection of cyclosporine was found to be 100 ng/ml and 200 ng/ml. Recovery was found to be in the range of 98.08-101.55%. The proposed method was successfully applied for the quantitative determination of cyclosporine in a capsule dosage form.

Photodynamic therapy adjuvant to surgery in metastatic carcinoma in brain

Cerebral metastases occur in 15-40% of cancers and their incidence is increasing. We have studied the use of fluorescence image-guided surgery and repetitive photodynamic therapy in 14 metastatic brain cancers.

METHODS

Case note review of prospectively collected data on patients who were treated with PDT at the time of surgery for brain metastases. Patients were consented for the surgery and PDT. Patients were given 2 mg/kg body weight of Photofrin IV 48 h before the surgery and 20 mg/kg 5-aminolevulenic acid orally 3h before surgery. Following resection of the tumor using fluorescence, microsurgical and image guidance techniques, the post-excision cavity is filled with a balloon using 0.32% intralipid solution and up to five consecutive PDT treatments were given using 100 J/cm(2) Diode Laser 630 nm. Patients were followed up clinically and by brain imaging every 3 months till their death.

RESULTS

Seven were lung in origin and seven of variable sources. One patient with lung metastases died of unrelated cause while the remaining six had remained free from brain disease till their death. Two of the remaining seven patients died of local brain recurrence, one bowel after 4 weeks and one of unknown primary after 70 weeks.

CONCLUSION

Adjuvant repetitive PDT seems to offer an excellent local control of metastatic brain carcinomas with about 79% of patients succumb to the primary and only two out of fourteen died of brain recurrence with the best results obtained in lung cancer.

Recurrent cerebral metastasis from a cardiac myxoma: case report and review of literature

We report the case of a 65-year-old patient who had complete excision of an atrial myxoma and subsequently presented to our unit with recurrent cerebral metastasis. The case demonstrates that cardiac myxoma despite benign histological appearance is capable of metastatic spread.

Age and biostratigraphic significance of the Punung Rainforest Fauna, East Java, Indonesia, and implications for Pongo and Homo

The Punung Fauna is a key component in the biostratigraphic sequence of Java. It represents the most significant faunal turnover on the island in the last 1.5 million years, when Stegodon and other archaic mammal species characteristic of earlier Faunal stages were replaced by a fully modern fauna that included rainforest-dependent species such as Pongo pygmaeus (orangutan). Here, we report the first numerical ages for the Punung Fauna obtained by luminescence and uranium-series dating of the fossil-bearing deposits and associated flowstones. The Punung Fauna contained in the dated breccia is of early Last Interglacial age (between 128+/-15 and 118+/-3 ka). This result has implications for the age of the preceding Ngandong Fauna, including Homo erectus remains found in the Ngandong Terrace, and for the timing of Homo sapiens arrival in Southeast Asia, in view of claims for a modern human tooth associated with the Punung breccia.

De novo meningioma arising at a previous burr hole site

A case of meningioma that developed at a site of a previously performed burr hole is reported. To the best of our knowledge, this is the first reported case of a meningioma arising at a previous burr hole site. The possible reasons for its causation are discussed.

Relocation of the 1936 Mojokerto skull discovery site near Perning, East Java

The fossil calvaria known as the Mojokerto child's skull was discovered in 1936, but uncertainties have persisted about its paleoenvironmental context and geological age because of difficulties in relocating the discovery site. Past relocation efforts were hindered by inaccuracies in old base maps, intensive post-1930s agricultural terracing, and new tree and brush growth. Fortunately geologic cross sections and site photographs from 1936-1938-not fully utilized in past relocation fieldwork-closely circumscribe site geography and geology. These documents match the conditions at just one sandstone outcrop. It is situated on the southern margin of a topographic nose at the upper end of a approximately 18 m-wide gully ( approximately 0663760 m E, 9183430 m N, UTM Zone 49 M), approximately 15 m southeast of the Kumai et al. (1985) relocation. The relocated discovery bed is approximately 3.3 m of fossiliferous pebbly sandstone, a river-channel deposit cut into tuffaceous mudstone. The sandstone and mudstone beds correspond to original site descriptions. Pebbly sandstone is also found within the skull. The calvaria is well-preserved and taphonomically similar to large and fragile specimens found among several hundred vertebrate fossils excavated from the sandstone in 2001-2002. Since no well-preserved fossils were found intact at the surface of the sandstone, the good condition of the Mojokerto skull suggests that it was buried fully when discovered. The relocated hominin bed is the uppermost fluvial sandstone of a marine-deltaic sequence in the upper Pucangan Formation. The Mojokerto child probably died along the ancient seacoast, judging from the large extent of the deltaic facies and evidence that the calvaria experienced minimal transport. The relocated discovery bed is approximately 20 m stratigraphically above the horizon from which the widely cited 1.81+/-0.04 Ma (40)Ar/(39)Ar date for the skull was obtained. Additional field and laboratory results will be required to determine the skull's age.

Fine needle aspiration of unilocular ovarian cysts--a cytohistological correlation

OBJECTIVE

To assess the diagnostic efficacy of Fine Needle Aspiration Cytology in differentiating neoplastic and non-neoplastic dysfunctional ovarian cysts--cytological findings to be verified with histology of excised cyst.

METHODS

In this prospective study fifty-three cases with unilocular nonseptate ovarian cystic masses,detected on ultrasound examination were subjected to ultrasound guided fine needle aspiration of the cyst contents at Department of Pathology, Allama Iqbal Medical College Lahore, from January 1999 - January 2000. Aspirated fluid was examined cytologicaly using Giemsa stain on the smears prepared from centrifuged deposit. The same cyst removed surgically was examined histologicaly and cytohistological correlation was carried out.

RESULTS

In this series of 53 cases, histologically confirmed break up of various types of cysts was follicular cysts (n=25), leuteal cysts (n=6), serous cysts (n=15), mucous cysts (N=4) and Endometriotic cysts (n=3). Non-diagnostic fine needle aspirate was obtained in 19/53 cases, majority being the follicular cysts. The cytohistological correlation revealed no false positive but 40% false negative results for follicular cysts on cytological examination of the aspirate. Hence the specificity and sensitivity for cytological diagnosis of follicular cyst was 100% and 60% respectively. For leuteal cysts, false positive and false negative results on cytological examination were 0% and 16.6% respectively with a specificity and sensitivity value of 100% and 83% respectively. For neoplastic serous cysts cytologically false positive and false negative diagnosis was 0% and 46.6% respectively with specificity and sensitivity of 100% and 53%. For mucinous cystadenomas sensitivity and specificity of cytological diagnosis was 100%. For endometriotic cysts a sensitivity of 67% and specificity of 100% was procured with cytological evaluation.

CONCLUSION

Guided fine needle aspiration cytology may prove to be one of the most valuable and acceptable tools in the differential diagnosis of ovarian cystic lesions.