Change in Cationic Amino Acid Transport System and Effect of Lysine Pretreatment on Inflammatory State in Amyotrophic Lateral Sclerosis Cell Model

( Sana Latif ),( Young-sook Kang ) 한국응용약물학회 2021 Biomolecules & Therapeutics(구 응용약물학회지) Vol.29 No.5

Amyotrophic lateral sclerosis (ALS) is a lethal neurological disorder characterized by the deterioration of motor neurons. The aim of this study was to investigate alteration of cationic amino acid transporter (CAT-1) activity in the transport of lysine and the pretreatment effect of lysine on pro-inflammatory states in an amyotrophic lateral sclerosis cell line. The mRNA expression of cationic amino acid transporter 1 was lower in NSC-34/hSOD1^G93A (MT) than the control cell line (WT), lysine transport is mediated by CAT-1 in NSC-34 cell lines. The uptake of [³H]L-lysine was Na+-independent, voltage-sensitive, and strongly inhibited by inhibitors and substrates of cationic amino acid transporter 1 (system y⁺). The transport process involved two saturable processes in both cell lines. In the MT cell line, at a high-affinity site, the affinity was 9.4-fold higher and capacity 24-fold lower than that in the WT; at a low-affinity site, the capacity was 2.3-fold lower than that in the WT cell line. Donepezil and verapamil competitively inhibited [³H]L-lysine uptake in the NSC-34 cell lines. Pretreatment with pro-inflammatory cytokines decreased the uptake of [³H]L-lysine and mRNA expression levels in both cell lines; however, the addition of L-lysine restored the transport activity in the MT cell lines. L-Lysine exhibited neuroprotective effects against pro-inflammatory states in the ALS disease model cell lines. In conclusion, studying the alteration in the expression of transporters and characteristics of lysine transport in ALS can lead to the development of new therapies for neurodegenerative diseases.

The effects of pro-inflammatory cytokines and glutamate on l-arginine transport in a human microglial cell line (HMC-3)

Latif Sana,Kang Young-Sook 한국약제학회 2024 Journal of Pharmaceutical Investigation Vol.54 No.1

Purpose Neuroinflammation and microglial activation are the key factors in neurodegenerative motor neuron diseases. Microglial activation has been link to disease progression in humans. This study investigated the transport and potential effects of l-arginine under pathological conditions in a human microglial cell line. Methods Transport study was performed using [ 3H]l-arginine isotope and uptake was measured in the human microglial clone 3 (HMC-3) cell line. MTT assay was performed for cell viability analysis. mRNA expression of cationic amino acid transporter-1 (CAT-1) was also determined. Results The transport of l-arginine was time- and concentration-dependent in HMC-3 cell lines. Kinetic parameters revealed carrier-mediated arginine transport via CAT-1, with high affinity and low capacity at high-affinity sites and low affinity and high capacity at low-affinity sites. HMC-3 cells pretreated with glutamate exhibited decreased viability. [ 3H]l-arginine uptake increased markedly with glutamate pretreatment, whereas co-treatment with arginine further increased the uptake. After tumor necrosis factor α and lipopolysaccharide pretreatment, [ 3H]l-arginine uptake significantly increased. In contrast, the uptake was significantly decreased after co-treatment of the cells with unlabeled l-arginine. The mRNA expression of CAT-1 showed a similar uptake pattern. Various pharmacological drugs including donepezil, quinidine, verapamil and tramadol significantly inhibited the uptake of [ 3H]l-arginine in HMC-3 cell line. Conclusion These results suggest that l-arginine supplementation may play an effective role against inflammatory states in neurodegenerative diseases.

Effect of Various Pathological Conditions on Nitric Oxide Level and L-Citrulline Uptake in Motor Neuron-Like (NSC-34) Cell Lines

( Shashi Gautam ),( Sana Latif ),( Young-sook Kang ) 한국응용약물학회 2024 Biomolecules & Therapeutics(구 응용약물학회지) Vol.32 No.1

Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron disorder that causes progressive paralysis. L-Citrulline is a nonessential neutral amino acid produced by L-arginine via nitric oxide synthase (NOS). According to previous studies, the pathogenesis of ALS entails glutamate toxicity, oxidative stress, protein misfolding, and neurofilament disruption. In addition, L-citrulline prevents neuronal cell death in brain ischemia; therefore, we investigated the change in the transport of L-citrulline under various pathological conditions in a cell line model of ALS. We examined the uptake of [¹⁴C]L-citrulline in wild-type (hSOD1wt/WT) and mutant NSC-34/ SOD1G93A (MT) cell lines. The cell viability was determined via MTT assay. A transport study was performed to determine the uptake of [¹⁴C]L-citrulline. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was performed to determine the expression levels of rat large neutral amino acid transported 1 (rLAT1) in ALS cell lines. Nitric oxide (NO) assay was performed using Griess reagent. L-Citrulline had a restorative effect on glutamate induced cell death, and increased [¹⁴C]Lcitrulline uptake and mRNA levels of the large neutral amino acid transporter (LAT1) in the glutamate-treated ALS disease model (MT). NO levels increased significantly when MT cells were pretreated with glutamate for 24 h and restored by co-treatment with L-citrulline. Co-treatment of MT cells with L-arginine, an NO donor, increased NO levels. NSC-34 cells exposed to high glucose conditions showed a significant increase in [¹⁴C]L-citrulline uptake and LAT1 mRNA expression levels, which were restored to normal levels upon co-treatment with unlabeled L-citrulline. In contrast, exposure of the MT cell line to tumor necrosis factor alpha, lipopolysaccharides, and hypertonic condition decreased the uptake significantly which was restored to the normal level by co-treating with unlabeled L-citrulline. L-Citrulline can restore NO levels and cellular uptake in ALS-affected cells with glutamate cytotoxicity, pro-inflammatory cytokines, or other pathological states, suggesting that L-citrulline supplementation in ALS may play a key role in providing neuroprotection.

An Application of Machine Learning in Retail for Demand Forecasting

Muhammad Umer Farooq,Mustafa Latif,Waseemullah,Mirza Adnan Baig,Muhammad Ali Akhtar,Nuzhat Sana International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.9

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

An Application of Machine Learning in Retail for Demand Forecasting

Muhammad Umer Farooq,Mustafa Latif,Waseem,Mirza Adnan Baig,Muhammad Ali Akhtar,Nuzhat Sana International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.8

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

Differences in Neurotransmitters Level as Biomarker on Sleep Effects in Dementia Patients with Insomnia after Essential Oils Treatment

Lee Kyung-Bok,Latif Sana,Kang Young-Sook 한국응용약물학회 2023 Biomolecules & Therapeutics(구 응용약물학회지) Vol.31 No.3

This study aimed to examine the effects of several essential oils on insomnia in dementia patients following transdermal treatment (aromatherapy). The mean change rates (%) of sleep biomarkers were compared between the single essential and jojoba (vehicle) oil massage groups in this study. The lavender (L) essential oil massage group demonstrated a significant decrease in the mean change rate (%) of 24-h urinary free cortisol, whereas the valerian (V) essential oil massage group demonstrated a significant increase in the mean change rate (%) of serum 5-hydroxytryptamine. In addition, a significant increase in the mean change rate (%) of 24-h urinary norepinephrine was observed in the chamomile (C) essential oil massage group only. Based on these results, valerian, lavender, and chamomile oils were mixed in different ratios to produce blending oils A (L:C:V=2:2:1), B (L:C:V=3:1:1) and C (L:C:V=1:3:1). The highest level of serum 5-hydroxytryptamine was observed after administering blending oil A. These results suggest that blending oil A might possess therapeutic effects against insomnia. Overall, it is hypothesized that the optimally blended essential oil will produce synergic effects when combined with hypnotic drugs.